J. Frankel and S. King. Factoring Gaussian precision matrices for linear dynamic models. Pattern Recognition Letters, 28(16):2264-2272, December 2007. [ bib | DOI | .pdf ]

The linear dynamic model (LDM), also known as the Kalman filter model, has been the subject of research in the engineering, control, and more recently, machine learning and speech technology communities. The Gaussian noise processes are usually assumed to have diagonal, or occasionally full, covariance matrices. A number of recent papers have considered modelling the precision rather than covariance matrix of a Gaussian distribution, and this work applies such ideas to the LDM. A Gaussian precision matrix P can be factored into the form P = UTSU where U is a transform and S a diagonal matrix. By varying the form of U, the covariance can be specified as being diagonal or full, or used to model a given set of spatial dependencies. Furthermore, the transform and scaling components can be shared between models, allowing richer distributions with only marginally more parameters than required to specify diagonal covariances. The method described in this paper allows the construction of models with an appropriate number of parameters for the amount of available training data. We provide illustrative experimental results on synthetic and real speech data in which models with factored precision matrices and automatically-selected numbers of parameters are as good as or better than models with diagonal covariances on small data sets and as good as models with full covariance matrices on larger data sets.

Ö. Çetin, M. Magimai-Doss, A. Kantor, S. King, C. Bartels, J. Frankel, and K. Livescu. Monolingual and crosslingual comparison of tandem features derived from articulatory and phone MLPs. In Proc. ASRU, Kyoto, December 2007. IEEE. [ bib | .pdf ]

In recent years, the features derived from posteriors of a multilayer perceptron (MLP), known as tandem features, have proven to be very effective for automatic speech recognition. Most tandem features to date have relied on MLPs trained for phone classification. We recently showed on a relatively small data set that MLPs trained for articulatory feature classification can be equally effective. In this paper, we provide a similar comparison using MLPs trained on a much larger data set - 2000 hours of English conversational telephone speech. We also explore how portable phone- and articulatory feature- based tandem features are in an entirely different language - Mandarin - without any retraining. We find that while phone-based features perform slightly better in the matched-language condition, they perform significantly better in the cross-language condition. Yet, in the cross-language condition, neither approach is as effective as the tandem features extracted from an MLP trained on a relatively small amount of in-domain data. Beyond feature concatenation, we also explore novel observation modelling schemes that allow for greater flexibility in combining the tandem and standard features at hidden Markov model (HMM) outputs.

Songfang Huang and Steve Renals. Hierarchical Pitman-Yor language models for ASR in meetings. In Proc. IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU'07), pages 124-129, Kyoto, Japan, December 2007. [ bib | .pdf ]

In this paper we investigate the application of a novel technique for language modeling - a hierarchical Bayesian language model (LM) based on the Pitman-Yor process - on automatic speech recognition (ASR) for multiparty meetings. The hierarchical Pitman-Yor language model (HPYLM), which was originally proposed in the machine learning field, provides a Bayesian interpretation to language modeling. An approximation to the HPYLM recovers the exact formulation of the interpolated Kneser-Ney smoothing method in n-gram models. This paper focuses on the application and scalability of HPYLM on a practical large vocabulary ASR system. Experimental results on NIST RT06s evaluation meeting data verify that HPYLM is a competitive and promising language modeling technique, which consistently performs better than interpolated Kneser-Ney and modified Kneser-Ney n-gram LMs in terms of both perplexity (PPL) and word error rate (WER).

K. Richmond. Trajectory mixture density networks with multiple mixtures for acoustic-articulatory inversion. In M. Chetouani, A. Hussain, B. Gas, M. Milgram, and J.-L. Zarader, editors, Advances in Nonlinear Speech Processing, International Conference on Non-Linear Speech Processing, NOLISP 2007, volume 4885 of Lecture Notes in Computer Science, pages 263-272. Springer-Verlag Berlin Heidelberg, December 2007. [ bib | DOI | .pdf ]

We have previously proposed a trajectory model which is based on a mixture density network (MDN) trained with target variables augmented with dynamic features together with an algorithm for estimating maximum likelihood trajectories which respects the constraints between those features. In this paper, we have extended that model to allow diagonal covariance matrices and multiple mixture components in the trajectory MDN output probability density functions. We have evaluated this extended model on an inversion mapping task and found the trajectory model works well, outperforming smoothing of equivalent trajectories using low-pass filtering. Increasing the number of mixture components in the TMDN improves results further.

J. Frankel, M. Wester, and S. King. Articulatory feature recognition using dynamic Bayesian networks. Computer Speech & Language, 21(4):620-640, October 2007. [ bib | .pdf ]

We describe a dynamic Bayesian network for articulatory feature recognition. The model is intended to be a component of a speech recognizer that avoids the problems of conventional “beads-on-a-string” phoneme-based models. We demonstrate that the model gives superior recognition of articulatory features from the speech signal compared with a stateof- the art neural network system. We also introduce a training algorithm that offers two major advances: it does not require time-aligned feature labels and it allows the model to learn a set of asynchronous feature changes in a data-driven manner.

Takashi Nose, Junichi Yamagishi, and Takao Kobayashi. A style control technique for HMM-based expressive speech synthesis. IEICE Trans. Information and Systems, E90-D(9):1406-1413, September 2007. [ bib | http ]

This paper describes a technique for controlling the degree of expressivity of a desired emotional expression and/or speaking style of synthesized speech in an HMM-based speech synthesis framework. With this technique, multiple emotional expressions and speaking styles of speech are modeled in a single model by using a multiple-regression hidden semi-Markov model (MRHSMM). A set of control parameters, called the style vector, is defined, and each speech synthesis unit is modeled by using the MRHSMM, in which mean parameters of the state output and duration distributions are expressed by multiple-regression of the style vector. In the synthesis stage, the mean parameters of the synthesis units are modified by transforming an arbitrarily given style vector that corresponds to a point in a low-dimensional space, called style space, each of whose coordinates represents a certain specific speaking style or emotion of speech. The results of subjective evaluation tests show that style and its intensity can be controlled by changing the style vector

J. Frankel, M. Magimai-Doss, S. King, K. Livescu, and Ö. Çetin. Articulatory feature classifiers trained on 2000 hours of telephone speech. In Proc. Interspeech, Antwerp, Belgium, August 2007. [ bib | .pdf ]

This paper is intended to advertise the public availability of the articulatory feature (AF) classification multi-layer perceptrons (MLPs) which were used in the Johns Hopkins 2006 summer workshop. We describe the design choices, data preparation, AF label generation, and the training of MLPs for feature classification on close to 2000 hours of telephone speech. In addition, we present some analysis of the MLPs in terms of classification accuracy and confusions along with a brief summary of the results obtained during the workshop using the MLPs. We invite interested parties to make use of these MLPs.

Maria Wolters, Pauline Campbell, Christine DePlacido, Amy Liddell, and David Owens. The effect of hearing loss on the intelligibility of synthetic speech. In Proc. Intl. Conf. Phon. Sci., August 2007. [ bib | .pdf ]

Many factors affect the intelligibility of synthetic speech. One aspect that has been severely neglected in past work is hearing loss. In this study, we investigate whether pure-tone audiometry thresholds across a wide range of frequencies (0.25-20kHz) are correlated with participants' performance on a simple task that involves accurately recalling and processing reminders. Participants' scores correlate not only with thresholds in the frequency ranges commonly associated with speech, but also with extended high-frequency thresholds.

Junichi Yamagishi, Takao Kobayashi, Steve Renals, Simon King, Heiga Zen, Tomoki Toda, and Keiichi Tokuda. Improved average-voice-based speech synthesis using gender-mixed modeling and a parameter generation algorithm considering GV. In Proc. 6th ISCA Workshop on Speech Synthesis (SSW-6), August 2007. [ bib | .pdf ]

For constructing a speech synthesis system which can achieve diverse voices, we have been developing a speaker independent approach of HMM-based speech synthesis in which statistical average voice models are adapted to a target speaker using a small amount of speech data. In this paper, we incorporate a high-quality speech vocoding method STRAIGHT and a parameter generation algorithm with global variance into the system for improving quality of synthetic speech. Furthermore, we introduce a feature-space speaker adaptive training algorithm and a gender mixed modeling technique for conducting further normalization of the average voice model. We build an English text-to-speech system using these techniques and show the performance of the system.

Maria Wolters, Pauline Campbell, Christine DePlacido, Amy Liddell, and David Owens. Making synthetic speech accessible to older people. In Proc. Sixth ISCA Workshop on Speech Synthesis, Bonn, Germany, August 2007. [ bib | .pdf ]

In this paper, we report on an experiment that tested users' ability to understand the content of spoken auditory reminders. Users heard meeting reminders and medication reminders spoken in both a natural and a synthetic voice. Our results show that older users can understand synthetic speech as well as younger users provided that the prompt texts are well-designed, using familiar words and contextual cues. As soon as unfamiliar and complex words are introduced, users' hearing affects how well they can understand the synthetic voice, even if their hearing would pass common screening tests for speech synthesis experiments. Although hearing thresholds correlate best with users' performance, central auditory processing may also influence performance, especially when complex errors are made.

Toshio Hirai, Junichi Yamagishi, and Seiichi Tenpaku. Utilization of an HMM-based feature generation module in 5 ms segment concatenative speech synthesis. In Proc. 6th ISCA Workshop on Speech Synthesis (SSW-6), August 2007. [ bib ]

If a concatenative speech synthesis system uses more short speech segments, it increases the potential to generate natural speech because the concatenation variation becomes greater. Recently, a synthesis approach was proposed in which very short (5 ms) segments are used. In this paper, an implementation of an HMM-based feature generation module into a very short segment concatenative synthesis system that has the advantage of modularity and a synthesis experiment are described.

Robert A. J. Clark, Monika Podsiadlo, Mark Fraser, Catherine Mayo, and Simon King. Statistical analysis of the Blizzard Challenge 2007 listening test results. In Proc. Blizzard 2007 (in Proc. Sixth ISCA Workshop on Speech Synthesis), Bonn, Germany, August 2007. [ bib | .pdf ]

Blizzard 2007 is the third Blizzard Challenge, in which participants build voices from a common dataset. A large listening test is conducted which allows comparison of systems in terms of naturalness and intelligibility. New sections were added to the listening test for 2007 to test the perceived similarity of the speaker's identity between natural and synthetic speech. In this paper, we present the results of the listening test and the subsequent statistical analysis.

Keywords: Blizzard

Maria Wolters, Pauline Campbell, Christine DePlacido, Amy Liddell, and David Owens. The role of outer hair cell function in the perception of synthetic versus natural speech. In Proc. Interspeech, August 2007. [ bib | .pdf ]

Hearing loss as assessed by pure-tone audiometry (PTA) is significantly correlated with the intelligibility of synthetic speech. However, PTA is a subjective audiological measure that assesses the entire auditory pathway and does not discriminate between the different afferent and efferent contributions. In this paper, we focus on one particular aspect of hearing that has been shown to correlate with hearing loss: outer hair cell (OHC) function. One role of OHCs is to increase sensitivity and frequency selectivity. This function of OHCs can be assessed quickly and objectively through otoacoustic emissions (OAE) testing, which is little known outside the field of audiology. We find that OHC function affects the perception of human speech, but not that of synthetic speech. This has important implications not just for audiological and electrophysiological research, but also for adapting speech synthesis to ageing ears.

Mark Fraser and Simon King. The Blizzard Challenge 2007. In Proc. Blizzard 2007 (in Proc. Sixth ISCA Workshop on Speech Synthesis), Bonn, Germany, August 2007. [ bib | .pdf ]

In Blizzard 2007, the third Blizzard Challenge, participants were asked to build voices from a dataset, a defined subset and, following certain constraints, a subset of their choice. A set of test sentences was then released to be synthesised. An online evaluation of the submitted synthesised sentences focused on naturalness and intelligibility, and added new sec- tions for degree of similarity to the original speaker, and similarity in terms of naturalness of pairs of sentences from different systems. We summarise this year's Blizzard Challenge and look ahead to possible designs for Blizzard 2008 in the light of participant and listener feedback.

Keywords: Blizzard

Heiga Zen, Takashi Nose, Junichi Yamagishi, Shinji Sako, Takashi Masuko, Alan Black, and Keiichi Tokuda. The HMM-based speech synthesis system (HTS) version 2.0. In Proc. 6th ISCA Workshop on Speech Synthesis (SSW-6), August 2007. [ bib ]

A statistical parametric speech synthesis system based on hidden Markov models (HMMs) has grown in popularity over the last few years. This system simultaneously models spectrum, excitation, and duration of speech using context-dependent HMMs and generates speech waveforms from the HMMs themselves. Since December 2002, we have publicly released an open-source software toolkit named HMM-based speech synthesis system (HTS) to provide a research and development platform for the speech synthesis community. In December 2006, HTS version 2.0 was released. This version includes a number of new features which are useful for both speech synthesis researchers and developers. This paper describes HTS version 2.0 in detail, as well as future release plans.

Makoto Tachibana, Keigo Kawashima, Junichi Yamagishi, and Takao Kobayashi. Performance evaluation of HMM-based style classification with a small amount of training data. In Proc. Interspeech 2007, August 2007. [ bib ]

This paper describes a classification technique for emotional expressions and speaking styles of speech using only a small amount of training data of a target speaker. We model spectral and fundamental frequency (F0) features simultaneously using multi-space probability distribution HMM (MSD-HMM), and adapt a speaker-independent neutral style model to a certain target speaker’s style model with a small amount of data using MSD-MLLR which is extended MLLR for MSD-HMM. We perform classification experiments for professional narrators’ speech and non-professional speakers' speech and evaluate the performance of proposed technique by comparing with other commonly used classifiers. We show that the proposed technique gives better result than the other classifiers when using a few sentences of target speaker’s style data.

Volker Strom, Ani Nenkova, Robert Clark, Yolanda Vazquez-Alvarez, Jason Brenier, Simon King, and Dan Jurafsky. Modelling prominence and emphasis improves unit-selection synthesis. In Proc. Interspeech 2007, Antwerp, Belgium, August 2007. [ bib | .pdf ]

We describe the results of large scale perception experiments showing improvements in synthesising two distinct kinds of prominence: standard pitch-accent and strong emphatic accents. Previously prominence assignment has been mainly evaluated by computing accuracy on a prominence-labelled test set. By contrast we integrated an automatic pitch-accent classifier into the unit selection target cost and showed that listeners preferred these synthesised sentences. We also describe an improved recording script for collecting emphatic accents, and show that generating emphatic accents leads to further improvements in the fiction genre over incorporating pitch accent only. Finally, we show differences in the effects of prominence between child-directed speech and news and fiction genres. Index Terms: speech synthesis, prosody, prominence, pitch accent, unit selection

Gregor Hofer and Hiroshi Shimodaira. Automatic head motion prediction from speech data. In Proc. Interspeech 2007, Antwerp, Belgium, August 2007. [ bib | .pdf ]

In this paper we present a novel approach to generate a sequence of head motion units given some speech. The modelling approach is based on the notion that head motion can be divided into a number of short homogeneous units that can each be modelled individually. The system is based on Hidden Markov Models (HMM), which are trained on motion units and act as a sequence generator. They can be evaluated by an accuracy measure. A database of motion capture data was collected and manually annotated for head motion and is used to train the models. It was found that the model is good at distinguishing high activity regions from regions with less activity with accuracies around 75 percent. Furthermore the model is able to distinguish different head motion patterns based on speech features somewhat reliably, with accuracies reaching almost 70 percent.

Heriberto Cuayáhuitl, Steve Renals, Oliver Lemon, and Hiroshi Shimodaira. Hierarchical dialogue optimization using semi-markov decision processes. In Proc. Interspeech, August 2007. [ bib | .pdf ]

This paper addresses the problem of dialogue optimization on large search spaces. For such a purpose, in this paper we propose to learn dialogue strategies using multiple Semi-Markov Decision Processes and hierarchical reinforcement learning. This approach factorizes state variables and actions in order to learn a hierarchy of policies. Our experiments are based on a simulated flight booking dialogue system and compare flat versus hierarchical reinforcement learning. Experimental results show that the proposed approach produced a dramatic search space reduction (99.36%), and converged four orders of magnitude faster than flat reinforcement learning with a very small loss in optimality (on average 0.3 system turns). Results also report that the learnt policies outperformed a hand-crafted one under three different conditions of ASR confidence levels. This approach is appealing to dialogue optimization due to faster learning, reusable subsolutions, and scalability to larger problems.

K. Richmond. A multitask learning perspective on acoustic-articulatory inversion. In Proc. Interspeech, Antwerp, Belgium, August 2007. [ bib | .pdf ]

This paper proposes the idea that by viewing an inversion mapping MLP from a Multitask Learning perspective, we may be able to relax two constraints which are inherent in using electromagnetic articulography as a source of articulatory information for speech technology purposes. As a first step to evaluating this idea, we perform an inversion mapping experiment in an attempt to ascertain whether the hidden layer of a “multitask” MLP can act beneficially as a hidden representation that is shared between inversion mapping subtasks for multiple articulatory targets. Our results in the case of the tongue dorsum x-coordinate indicate this is indeed the case and show good promise. Results for the tongue dorsum y-coordinate however are not so clear-cut, and will require further investigation.

Peter Bell and Simon King. Sparse gaussian graphical models for speech recognition. In Proc. Interspeech 2007, Antwerp, Belgium, August 2007. [ bib | .pdf ]

We address the problem of learning the structure of Gaussian graphical models for use in automatic speech recognition, a means of controlling the form of the inverse covariance matrices of such systems. With particular focus on data sparsity issues, we implement a method for imposing graphical model structure on a Gaussian mixture system, using a convex optimisation technique to maximise a penalised likelihood expression. The results of initial experiments on a phone recognition task show a performance improvement over an equivalent full-covariance system.

Junichi Yamagishi, Heiga Zen, Tomoki Toda, and Keiichi Tokuda. Speaker-independent HMM-based speech synthesis system - HTS-2007 system for the Blizzard Challenge 2007. In Proc. Blizzard Challenge 2007, August 2007. [ bib | .pdf ]

This paper describes an HMM-based speech synthesis system developed by the HTS working group for the Blizzard Challenge 2007. To further explore the potential of HMM-based speech synthesis, we incorporate new features in our conventional system which underpin a speaker-independent approach: speaker adaptation techniques; adaptive training for HSMMs; and full covariance modeling using the CSMAPLR transforms.

K. Richmond, V. Strom, R. Clark, J. Yamagishi, and S. Fitt. Festival multisyn voices for the 2007 blizzard challenge. In Proc. Blizzard Challenge Workshop (in Proc. SSW6), Bonn, Germany, August 2007. [ bib | .pdf ]

This paper describes selected aspects of the Festival Multisyn entry to the Blizzard Challenge 2007. We provide an overview of the process of building the three required voices from the speech data provided. This paper focuses on new features of Multisyn which are currently under development and which have been employed in the system used for this Blizzard Challenge. These differences are the application of a more flexible phonetic lattice representation during forced alignment labelling and the use of a pitch accent target cost component. Finally, we also examine aspects of the speech data provided for this year's Blizzard Challenge and raise certain issues for discussion concerning the aim of comparing voices made with differing subsets of the data provided.

David Owens, Pauline Campbell, Amy Liddell, Christine DePlacido, and Maria Wolters. Random gap detection threshold: A useful measure of auditory ageing? In Proc. Europ. Cong. Fed. Audiol. Heidelberg, Germany, June 2007. [ bib | .pdf ]

Amy Liddell, David Owens, Pauline Campbell, Christine DePlacido, and Maria Wolters. Can extended high frequency hearing thresholds be used to detect auditory processing difficulties in an ageing population? In Proc. Europ. Cong. Fed. Audiol. Heidelberg, Germany, June 2007. [ bib ]

Marilyn McGee-Lennon, Maria Wolters, and Tony McBryan. Auditory reminders in the home. In Proc. Intl. Conf. Auditory Display (ICAD), Montreal, Canada, June 2007. [ bib ]

Ö. Çetin, A. Kantor, S. King, C. Bartels, M. Magimai-Doss, J. Frankel, and K. Livescu. An articulatory feature-based tandem approach and factored observation modeling. In Proc. ICASSP, Honolulu, April 2007. [ bib | .pdf ]

The so-called tandem approach, where the posteriors of a multilayer perceptron (MLP) classifier are used as features in an automatic speech recognition (ASR) system has proven to be a very effective method. Most tandem approaches up to date have relied on MLPs trained for phone classification, and appended the posterior features to some standard feature hidden Markov model (HMM). In this paper, we develop an alternative tandem approach based on MLPs trained for articulatory feature (AF) classification. We also develop a factored observation model for characterizing the posterior and standard features at the HMM outputs, allowing for separate hidden mixture and state-tying structures for each factor. In experiments on a subset of Switchboard, we show that the AFbased tandem approach is as effective as the phone-based approach, and that the factored observation model significantly outperforms the simple feature concatenation approach while using fewer parameters.

K. Livescu, Ö. Çetin, M. Hasegawa-Johnson, S. King, C. Bartels, N. Borges, A. Kantor, P. Lal, L. Yung, S. Bezman, Dawson-Haggerty, B. Woods, J. Frankel, M. Magimai-Doss, and K. Saenko. Articulatory feature-based methods for acoustic and audio-visual speech recognition: Summary from the 2006 JHU Summer Workshop. In Proc. ICASSP, Honolulu, April 2007. [ bib | .pdf ]

We report on investigations, conducted at the 2006 Johns HopkinsWorkshop, into the use of articulatory features (AFs) for observation and pronunciation models in speech recognition. In the area of observation modeling, we use the outputs of AF classiers both directly, in an extension of hybrid HMM/neural network models, and as part of the observation vector, an extension of the tandem approach. In the area of pronunciation modeling, we investigate a model having multiple streams of AF states with soft synchrony constraints, for both audio-only and audio-visual recognition. The models are implemented as dynamic Bayesian networks, and tested on tasks from the Small-Vocabulary Switchboard (SVitchboard) corpus and the CUAVE audio-visual digits corpus. Finally, we analyze AF classication and forced alignment using a newly collected set of feature-level manual transcriptions.

A. Dielmann and S. Renals. DBN based joint dialogue act recognition of multiparty meetings. In Proc. IEEE ICASSP, volume 4, pages 133-136, April 2007. [ bib | .pdf ]

Joint Dialogue Act segmentation and classification of the new AMI meeting corpus has been performed through an integrated framework based on a switching dynamic Bayesian network and a set of continuous features and language models. The recognition process is based on a dictionary of 15 DA classes tailored for group decision-making. Experimental results show that a novel interpolated Factored Language Model results in a low error rate on the automatic segmentation task, and thus good recognition results can be achieved on AMI multiparty conversational speech.

K. Livescu, A. Bezman, N. Borges, L. Yung, Ö. Çetin, J. Frankel, S. King, M. Magimai-Doss, X. Chi, and L. Lavoie. Manual transcription of conversational speech at the articulatory feature level. In Proc. ICASSP, Honolulu, April 2007. [ bib | .pdf ]

We present an approach for the manual labeling of speech at the articulatory feature level, and a new set of labeled conversational speech collected using this approach. A detailed transcription, including overlapping or reduced gestures, is useful for studying the great pronunciation variability in conversational speech. It also facilitates the testing of feature classiers, such as those used in articulatory approaches to automatic speech recognition. We describe an effort to transcribe a small set of utterances drawn from the Switchboard database using eight articulatory tiers. Two transcribers have labeled these utterances in a multi-pass strategy, allowing for correction of errors. We describe the data collection methods and analyze the data to determine how quickly and reliably this type of transcription can be done. Finally, we demonstrate one use of the new data set by testing a set of multilayer perceptron feature classiers against both the manual labels and forced alignments.

Junichi Yamagishi and Takao Kobayashi. Average-voice-based speech synthesis using hsmm-based speaker adaptation and adaptive training. IEICE Trans. Information and Systems, E90-D(2):533-543, February 2007. [ bib ]

In speaker adaptation for speech synthesis, it is desirable to convert both voice characteristics and prosodic features such as F0 and phone duration. For simultaneous adaptation of spectrum, F0 and phone duration within the HMM framework, we need to transform not only the state output distributions corresponding to spectrum and F0 but also the duration distributions corresponding to phone duration. However, it is not straightforward to adapt the state duration because the original HMM does not have explicit duration distributions. Therefore, we utilize the framework of the hidden semi-Markov model (HSMM), which is an HMM having explicit state duration distributions, and we apply an HSMM-based model adaptation algorithm to simultaneously transform both the state output and state duration distributions. Furthermore, we propose an HSMM-based adaptive training algorithm to simultaneously normalize the state output and state duration distributions of the average voice model. We incorporate these techniques into our HSMM-based speech synthesis system, and show their effectiveness from the results of subjective and objective evaluation tests.

S. King, J. Frankel, K. Livescu, E. McDermott, K. Richmond, and M. Wester. Speech production knowledge in automatic speech recognition. Journal of the Acoustical Society of America, 121(2):723-742, February 2007. [ bib | .pdf ]

Although much is known about how speech is produced, and research into speech production has resulted in measured articulatory data, feature systems of different kinds and numerous models, speech production knowledge is almost totally ignored in current mainstream approaches to automatic speech recognition. Representations of speech production allow simple explanations for many phenomena observed in speech which cannot be easily analyzed from either acoustic signal or phonetic transcription alone. In this article, we provide a survey of a growing body of work in which such representations are used to improve automatic speech recognition.

Leonardo Badino and Robert A.J. Clark. Issues of optionality in pitch accent placement. In Proc. 6th ISCA Speech Synthesis Workshop, Bonn, Germany, 2007. [ bib | .pdf ]

When comparing the prosodic realization of different English speakers reading the same text, a significant disagreement is usually found amongst the pitch accent patterns of the speakers. Assuming that such disagreement is due to a partial optionality of pitch accent placement, it has been recently proposed to evaluate pitch accent predictors by comparing them with multi-speaker reference data. In this paper we face the issue of pitch accent optionality at different levels. At first we propose a simple mathematical definition of intra-speaker optionality which allows us to introduce a function for evaluating pitch accent predictors which we show being more accurate and robust than those used in previous works. Subsequently we compare a pitch accent predictor trained on single speaker data with a predictor trained on multi-speaker data in order to point out the large overlapping between intra-speaker and inter-speaker optionality. Finally, we show our successful results in predicting intra-speaker optionality and we suggest how this achievement could be exploited to improve the performances of a unit selection text-to speech synthesis (TTS) system.

David Beaver, Brady Zack Clark, Edward Flemming, T. Florian Jaeger, and Maria Wolters. When semantics meets phonetics: Acoustical studies of second occurrence focus. Language, 83(2):245-276, 2007. [ bib | .pdf ]

A. Dielmann and S. Renals. Automatic dialogue act recognition using a dynamic Bayesian network. In S. Renals, S. Bengio, and J. Fiscus, editors, Proc. Multimodal Interaction and Related Machine Learning Algorithms Workshop (MLMI-06), pages 178-189. Springer, 2007. [ bib | .pdf ]

We propose a joint segmentation and classification approach for the dialogue act recognition task on natural multi-party meetings (ICSI Meeting Corpus). Five broad DA categories are automatically recognised using a generative Dynamic Bayesian Network based infrastructure. Prosodic features and a switching graphical model are used to estimate DA boundaries, in conjunction with a factored language model which is used to relate words and DA categories. This easily generalizable and extensible system promotes a rational approach to the joint DA segmentation and recognition task, and is capable of good recognition performance.

Songfang Huang and Steve Renals. Modeling prosodic features in language models for meetings. In A. Popescu-Belis, S. Renals, and H. Bourlard, editors, Machine Learning for Multimodal Interaction IV, volume 4892 of Lecture Notes in Computer Science, pages 191-202. Springer, 2007. [ bib | .pdf ]

Prosody has been actively studied as an important knowledge source for speech recognition and understanding. In this paper, we are concerned with the question of exploiting prosody for language models to aid automatic speech recognition in the context of meetings. Using an automatic syllable detection algorithm, the syllable-based prosodic features are extracted to form the prosodic representation for each word. Two modeling approaches are then investigated. One is based on a factored language model, which directly uses the prosodic representation and treats it as a `word'. Instead of direct association, the second approach provides a richer probabilistic structure within a hierarchical Bayesian framework by introducing an intermediate latent variable to represent similar prosodic patterns shared by groups of words. Four-fold cross-validation experiments on the ICSI Meeting Corpus show that exploiting prosody for language modeling can significantly reduce the perplexity, and also have marginal reductions in word error rate.

J. Yamagishi, T. Kobayashi, M. Tachibana, K. Ogata, and Y. Nakano. Model adaptation approach to speech synthesis with diverse voices and styles. In Proc. ICASSP, pages 1233-1236, 2007. [ bib ]

In human computer interaction and dialogue systems, it is often desirable for text-to-speech synthesis to be able to generate natural sounding speech with an arbitrary speaker Afs voice and with varying speaking styles and/or emotional expressions. We have developed an average-voice-based speech synthesis method using statistical average voice models and model adaptation techniques for this purpose. In this paper, we describe an overview of the speech synthesis system and show the current performance with several experimental results.

Alejandro Jaimes, Hervé Bourlard, Steve Renals, and Jean Carletta. Recording, indexing, summarizing, and accessing meeting videos: An overview of the AMI project. In Proc IEEE ICIAPW, pages 59-64, 2007. [ bib | DOI | http | .pdf ]

n this paper we give an overview of the AMI project. AMI developed the following: (1) an infrastructure for recording meetings using multiple microphones and cameras; (2) a one hundred hour, manually annotated meeting corpus; (3) a number of techniques for indexing, and summarizing of meeting videos using automatic speech recognition and computer vision, and (4) an extensible framework for browsing, and searching of meeting videos. We give an overview of the various techniques developed in AMI, their integration into our meeting browser framework, and future plans for AMIDA (Augmented Multiparty Interaction with Distant Access), the follow-up project to AMI.

Steve Renals, Thomas Hain, and Hervé Bourlard. Recognition and interpretation of meetings: The AMI and AMIDA projects. In Proc. IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU '07), 2007. [ bib | .pdf ]

The AMI and AMIDA projects are concerned with the recognition and interpretation of multiparty meetings. Within these projects we have: developed an infrastructure for recording meetings using multiple microphones and cameras; released a 100 hour annotated corpus of meetings; developed techniques for the recognition and interpretation of meetings based primarily on speech recognition and computer vision; and developed an evaluation framework at both component and system levels. In this paper we present an overview of these projects, with an emphasis on speech recognition and content extraction.

Gregor Hofer, Hiroshi Shimodaira, and Junichi Yamagishi. Speech-driven head motion synthesis based on a trajectory model. Poster at Siggraph 2007, 2007. [ bib | .pdf ]

Gabriel Murray and Steve Renals. Towards online speech summarization. In Proc. Interspeech '07, 2007. [ bib | .PDF ]

The majority of speech summarization research has focused on extracting the most informative dialogue acts from recorde d, archived data. However, a potential use case for speech sum- marization in the meetings domain is to facilitate a meeting in progress by providing the participants - whether they are at tend- ing in-person or remotely - with an indication of the most im- portant parts of the discussion so far. This requires being a ble to determine whether a dialogue act is extract-worthy befor e the global meeting context is available. This paper introduces a novel method for weighting dialogue acts using only very lim- ited local context, and shows that high summary precision is possible even when information about the meeting as a whole is lacking. A new evaluation framework consisting of weighted precision, recall and f-score is detailed, and the novel onl ine summarization method is shown to significantly increase recall and f-score compared with a method using no contextual infor- mation.

Ani Nenkova, Jason Brenier, Anubha Kothari, Sasha Calhoun, Laura Whitton, David Beaver, and Dan Jurafsky. To memorize or to predict: Prominence labeling in conversational speech. In NAACL Human Language Technology Conference, Rochester, NY, 2007. [ bib | .pdf ]

The immense prosodic variation of natural conversational speech makes it challenging to predict which words are prosodically prominent in this genre. In this paper, we examine a new feature, accent ratio, which captures how likely it is that a word will be realized as prominent or not. We compare this feature with traditional accentprediction features (based on part of speech and N-grams) as well as with several linguistically motivated and manually labeled information structure features, such as whether a word is given, new, or contrastive. Our results show that the linguistic features do not lead to significant improvements, while accent ratio alone can yield prediction performance almost as good as the combination of any other subset of features. Moreover, this feature is useful even across genres; an accent-ratio classifier trained only on conversational speech predicts prominence with high accuracy in broadcast news. Our results suggest that carefully chosen lexicalized features can outperform less fine-grained features.

Matthew P. Aylett, J. Sebastian Andersson, Leonardo Badino, and Christopher J. Pidcock. The Cerevoice Blizzard entry 2007: Are small database errors worse than compression artifacts? In Proc. Blizzard Challenge Workshop 2007, Bonn, Germany, 2007. [ bib | .pdf ]

In commercial systems the memory footprint of unit selection systems is often a key issue. This is especially true for PDAs and other embedded devices. In this year's Blizzard entry CereProc R gave itself the criteria that the full database system entered would have a smaller memory footprint than either of the two smaller database entries. This was accomplished by applying Speex speech compression to the full database entry. In turn a set of small database techniques used to improve the quality of small database systems in last years entry were extended. Finally, for all systems, two quality control methods were applied to the underlying database to improve the lexicon and transcription match to the underlying data. Results suggest that mild audio quality artifacts introduced by lossy compression have almost as much impact on MOS perceived quality as concatenation errors introduced by sparse data in the smaller systems with bulked diphones.

J. Frankel and S. King. Speech recognition using linear dynamic models. IEEE Transactions on Speech and Audio Processing, 15(1):246-256, January 2007. [ bib | .ps | .pdf ]

The majority of automatic speech recognition (ASR) systems rely on hidden Markov models, in which Gaussian mixtures model the output distributions associated with sub-phone states. This approach, whilst successful, models consecutive feature vectors (augmented to include derivative information) as statistically independent. Furthermore, spatial correlations present in speech parameters are frequently ignored through the use of diagonal covariance matrices. This paper continues the work of Digalakis and others who proposed instead a first-order linear state-space model which has the capacity to model underlying dynamics, and furthermore give a model of spatial correlations. This paper examines the assumptions made in applying such a model and shows that the addition of a hidden dynamic state leads to increases in accuracy over otherwise equivalent static models. We also propose a time-asynchronous decoding strategy suited to recognition with segment models. We describe implementation of decoding for linear dynamic models and present TIMIT phone recognition results.

Gabriel Murray and Steve Renals. Term-weighting for summarization of multi-party spoken dialogues. In A. Popescu-Belis, S. Renals, and H. Bourlard, editors, Machine Learning for Multimodal Interaction IV, volume 4892 of Lecture Notes in Computer Science, pages 155-166. Springer, 2007. [ bib | .pdf ]

This paper explores the issue of term-weighting in the genre of spontaneous, multi-party spoken dialogues, with the intent of using such term-weights in the creation of extractive meeting summaries. The field of text information retrieval has yielded many term-weighting tech- niques to import for our purposes; this paper implements and compares several of these, namely tf.idf, Residual IDF and Gain. We propose that term-weighting for multi-party dialogues can exploit patterns in word us- age among participant speakers, and introduce the su.idf metric as one attempt to do so. Results for all metrics are reported on both manual and automatic speech recognition (ASR) transcripts, and on both the ICSI and AMI meeting corpora.

Gregor Hofer, Hiroshi Shimodaira, and Junichi Yamagishi. Lip motion synthesis using a context dependent trajectory hidden Markov model. Poster at SCA 2007, 2007. [ bib | .pdf ]

J. Cabral, S. Renals, K. Richmond, and J. Yamagishi. Towards an improved modeling of the glottal source in statistical parametric speech synthesis. In Proc.of the 6th ISCA Workshop on Speech Synthesis, Bonn, Germany, 2007. [ bib | .pdf ]

This paper proposes the use of the Liljencrants-Fant model (LF-model) to represent the glottal source signal in HMM-based speech synthesis systems. These systems generally use a pulse train to model the periodicity of the excitation signal of voiced speech. However, this model produces a strong and uniform harmonic structure throughout the spectrum of the excitation which makes the synthetic speech sound buzzy. The use of a mixed band excitation and phase manipulation reduces this effect but it can result in degradation of the speech quality if the noise component is not weighted carefully. In turn, the LF-waveform has a decaying spectrum at higher frequencies, which is more similar to the real glottal source excitation signal. We conducted a perceptual experiment to test the hypothesis that the LF-model can perform as well as or better than the pulse train in a HMM-based speech synthesizer. In the synthesis, we used the mean values of the LF-parameters, calculated by measurements of the recorded speech. The result of this study is important not only regarding the improvement in speech quality of these type of systems, but also because the LF-model can be used to model many characteristics of the glottal source, such as voice quality, which are important for voice transformation and generation of expressive speech.

Sasha Calhoun. Predicting focus through prominence structure. In Proc. Interspeech, Antwerp, Belgium, 2007. [ bib | .pdf ]

Focus is central to our control of information flow in dialogue. Spoken language understanding systems therefore need to be able to detect focus automatically. It is well known that prominence is a key marker of focus in English, however, the relationship is not straight-forward. We present focus prediction models built using the NXT Switchboard corpus. We claim that a focus is more likely if a word is more prominent than expected given its syntactic, semantic and discourse properties. Crucially, the perception of prominence arises not only from acoustic cues, but also the position in prosodic structure. Our focus prediction results, along with a study showing the acoustic properties of focal accents vary by structural position, support our claims. As a largely novel task, these results are an important first step in detecting focus for spoken language applications.

Robert A. J. Clark, Korin Richmond, and Simon King. Multisyn: Open-domain unit selection for the Festival speech synthesis system. Speech Communication, 49(4):317-330, 2007. [ bib | DOI | .pdf ]

We present the implementation and evaluation of an open-domain unit selection speech synthesis engine designed to be flexible enough to encourage further unit selection research and allow rapid voice development by users with minimal speech synthesis knowledge and experience. We address the issues of automatically processing speech data into a usable voice using automatic segmentation techniques and how the knowledge obtained at labelling time can be exploited at synthesis time. We describe target cost and join cost implementation for such a system and describe the outcome of building voices with a number of different sized datasets. We show that, in a competitive evaluation, voices built using this technology compare favourably to other systems.

Alfred Dielmann and Steve Renals. Automatic meeting segmentation using dynamic Bayesian networks. IEEE Transactions on Multimedia, 9(1):25-36, 2007. [ bib | DOI | http | .pdf ]

Multiparty meetings are a ubiquitous feature of organizations, and there are considerable economic benefits that would arise from their automatic analysis and structuring. In this paper, we are concerned with the segmentation and structuring of meetings (recorded using multiple cameras and microphones) into sequences of group meeting actions such as monologue, discussion and presentation. We outline four families of multimodal features based on speaker turns, lexical transcription, prosody, and visual motion that are extracted from the raw audio and video recordings. We relate these low-level features to more complex group behaviors using a multistream modelling framework based on multistream dynamic Bayesian networks (DBNs). This results in an effective approach to the segmentation problem, resulting in an action error rate of 12.2%, compared with 43% using an approach based on hidden Markov models. Moreover, the multistream DBN developed here leaves scope for many further improvements and extensions.

T. Hain, L. Burget, J. Dines, G. Garau, M. Karafiat, M. Lincoln, J. Vepa, and V. Wan. The AMI System for the Transcription of Speech in Meetings. In Proc. ICASSP, 2007. [ bib | .pdf ]

This paper describes the AMI transcription system for speech in meetings developed in collaboration by five research groups. The system includes generic techniques such as discriminative and speaker adaptive training, vocal tract length normalisation, heteroscedastic linear discriminant analysis, maximum likelihood linear regression, and phone posterior based features, as well as techniques specifically designed for meeting data. These include segmentation and cross-talk suppression, beam-forming, domain adaptation, web-data collection, and channel adaptive training. The system was improved by more than 20% relative in word error rate compared to our previous system and was usd in the NIST RT’06 evaluations where it was found to yield competitive performance.

Heike Penner, Nicholas Miller, and Maria Wolters. Motor speech disorders in three Parkinsonian syndromes: A comparative study. In Proc. Intl. Conf. Phon. Sci,, 2007. [ bib ]