# aghoshal.bib

@article{lu2013,
author = {Lu, Liang and Chin, KK and Ghoshal, Arnab and Renals, Steve},
doi = {10.1109/TASL.2013.2248718},
title = {Joint Uncertainty Decoding for Noise Robust Subspace {Gaussian} Mixture Models},
journal = {IEEE Transactions on Audio, Speech and Language Processing},
number = {9},
pages = {1791--1804},
volume = {21},
year = {2013},
abstract = {Joint uncertainty decoding (JUD) is a model-based noise compensation technique for conventional Gaussian Mixture Model (GMM) based speech recognition systems. Unlike vector Taylor series (VTS) compensation which operates on the individual Gaussian components in an acoustic model, JUD clusters the Gaussian components into a smaller number of classes, sharing the compensation parameters for the set of Gaussians in a given class. This significantly reduces the computational cost. In this paper, we investigate noise compensation for subspace Gaussian mixture model (SGMM) based speech recognition systems using JUD. The total number of Gaussian components in an SGMM is typically very large. Therefore direct compensation of the individual Gaussian components, as performed by VTS, is computationally expensive. In this paper we show that JUD-based noise compensation can be successfully applied to SGMMs in a computationally efficient way. We evaluate the JUD/SGMM technique on the standard Aurora 4 corpus. Our experimental results indicate that the JUD/SGMM system results in lower word error rates compared with a conventional GMM system with either VTS-based or JUD-based noise compensation.}
}
@inproceedings{Swietojanski:ICASSP13,
author = {Swietojanski, Pawel and Ghoshal, Arnab and Renals, Steve},
doi = {10.1109/ICASSP.2013.6638967},
title = {Revisiting Hybrid and {GMM-HMM} system combination techniques},
booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
year = {2013},
abstract = {In this paper we investigate techniques to combine hybrid HMM-DNN (hidden Markov model -- deep neural network) and tandem HMM-GMM (hidden Markov model -- Gaussian mixture model) acoustic models using: (1) model averaging, and (2) lattice combination with Minimum Bayes Risk decoding. We have performed experiments on the TED Talks'' task following the protocol of the IWSLT-2012 evaluation. Our experimental results suggest that DNN-based and GMM- based acoustic models are complementary, with error rates being reduced by up to 8% relative when the DNN and GMM systems are combined at model-level in a multi-pass auto- matic speech recognition (ASR) system. Additionally, further gains were obtained by combining model-averaged lat- tices with the one obtained from baseline systems.},
categories = {deep neural networks, tandem, hybrid, system combination, TED}
}
@inproceedings{Ghoshal:ICASSP13,
author = {Ghoshal, Arnab and Swietojanski, Pawel and Renals, Steve},
doi = {10.1109/ICASSP.2013.6639084},
title = {Multilingual training of deep neural networks},
booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
year = {2013},
abstract = {We investigate multilingual modeling in the context of a deep neural network (DNN) -- hidden Markov model (HMM) hy- brid, where the DNN outputs are used as the HMM state like- lihoods. By viewing neural networks as a cascade of fea- ture extractors followed by a logistic regression classifier, we hypothesise that the hidden layers, which act as feature ex- tractors, will be transferable between languages. As a corol- lary, we propose that training the hidden layers on multiple languages makes them more suitable for such cross-lingual transfer. We experimentally confirm these hypotheses on the GlobalPhone corpus using seven languages from three dif- ferent language families: Germanic, Romance, and Slavic. The experiments demonstrate substantial improvements over a monolingual DNN-HMM hybrid baseline, and hint at av- enues of further exploration.},
categories = {Speech recognition, deep learning, neural networks, multilingual modeling}
}
@inproceedings{hasler2012,
author = {Hasler, Eva and Bell, Peter and Ghoshal, Arnab and Haddow, Barry and Koehn, Philipp and McInnes, Fergus and Renals, Steve and Swietojanski, Pawel},
abstract = {This paper describes the University of Edinburgh (UEDIN) systems for the IWSLT 2012 Evaluation. We participated in the ASR (English), MT (English-French, German-English) and SLT (English-French) tracks.},
title = {The {UEDIN} system for the {IWSLT} 2012 evaluation},
booktitle = {Proc. International Workshop on Spoken Language Translation},
year = {2012}
}
@inproceedings{swi2012_dnn,
author = {Swietojanski, P. and Ghoshal, A. and Renals, S.},
doi = {10.1109/SLT.2012.6424230},
title = {Unsupervised Cross-lingual knowledge transfer in {DNN-based LVCSR}},
booktitle = {Proc. IEEE Workshop on Spoken Language Technology},
month = {December},
pages = {246--251},
year = {2012},
abstract = {We investigate the use of cross-lingual acoustic data to initialise deep neural network (DNN) acoustic models by means of unsupervised restricted Boltzmann machine (RBM) pretraining. DNNs for German are pretrained using one or all of German, Portuguese, Spanish and Swedish. The DNNs are used in a tandem configuration, where the network outputs are used as features for a hidden Markov model (HMM) whose emission densities are modeled by Gaussian mixture models (GMMs), as well as in a hybrid configuration, where the network outputs are used as the HMM state likelihoods. The experiments show that unsupervised pretraining is more crucial for the hybrid setups, particularly with limited amounts of transcribed training data. More importantly, unsupervised pretraining is shown to be language-independent.}
}
@inproceedings{llu2012map,
author = {Lu, L. and Ghoshal, A. and Renals, S.},
doi = {10.1109/ICASSP.2012.6289012},
title = {{Maximum a posteriori adaptation of subspace Gaussian mixture models for cross-lingual speech recognition}},
booktitle = {Proc. ICASSP},
pages = {4877--4880},
year = {2012},
keywords = {Subspace Gaussian Mixture Model, Maximum a Posteriori Adaptation, Cross-lingual Speech Recognition},
abstract = {This paper concerns cross-lingual acoustic modeling in the case when there are limited target language resources. We build on an approach in which a subspace Gaussian mixture model (SGMM) is adapted to the target language by reusing the globally shared parameters estimated from out-of-language training data. In current cross-lingual systems, these parameters are fixed when training the target system, which can give rise to a mismatch between the source and target systems. We investigate a maximum a posteriori (MAP) adaptation approach to alleviate the potential mismatch. In particular, we focus on the adaptation of phonetic subspace parameters using a matrix variate Gaussian prior distribution. Experiments on the GlobalPhone corpus using the MAP adaptation approach results in word error rate reductions, compared with the cross-lingual baseline systems and systems updated using maximum likelihood, for training conditions with 1 hour and 5 hours of target language data.}
}
@article{lu_spl_2011,
author = {Lu, L. and Ghoshal, A. and Renals, S.},
title = {Regularized Subspace Gausian Mixture Models for Speech Recognition},
journal = {IEEE Signal Processing Letters},
number = {7},
pages = {419--422},
volume = {18},
year = {2011},
abstract = {Subspace Gaussian mixture models (SGMMs) provide a compact representation of the Gaussian parameters in an acoustic model, but may still suffer from over-fitting with insufficient training data. In this letter, the SGMM state parameters are estimated using a penalized maximum-likelihood objective, based on $\ell_1$ and $\ell_2$ regularization, as well as their combination, referred to as the elastic net, for robust model estimation. Experiments on the 5000-word Wall Street Journal transcription task show word error rate reduction and improved model robustness with regularization.},
categories = {Acoustic Modelling, Regularization, Sparsity, Subspace Gaussian Mixture Model}
}
@inproceedings{lu2012jud,
author = {Lu, L. and Ghoshal, A. and Renals, S.},
title = {{Joint uncertainty decoding with unscented transform for noise robust subspace Gaussian mixture model}},
booktitle = {Proc. Sapa-Scale workshop},
year = {2012},
keywords = {noise compensation, SGMM, JUD, UT},
abstract = {Common noise compensation techniques use vector Taylor series (VTS) to approximate the mismatch function. Recent work shows that the approximation accuracy may be improved by sampling. One such sampling technique is the unscented transform (UT), which draws samples deterministically from clean speech and noise model to derive the noise corrupted speech parameters. This paper applies UT to noise compensation of the subspace Gaussian mixture model (SGMM). Since UT requires relatively smaller number of samples for accurate estimation, it has significantly lower computational cost compared to other random sampling techniques. However, the number of surface Gaussians in an SGMM is typically very large, making the direct application of UT, for compensating individual Gaussian components, computationally impractical. In this paper, we avoid the computational burden by employing UT in the framework of joint uncertainty decoding (JUD), which groups all the Gaussian components into small number of classes, sharing the compensation parameters by class. We evaluate the JUD-UT technique for an SGMM system using the Aurora 4 corpus. Experimental results indicate that UT can lead to increased accuracy compared to VTS approximation if the JUD phase factor is untuned, and to similar accuracy if the phase factor is tuned empirically}
}
@inproceedings{lu2012noise,
author = {Lu, L. and Chin, KK and Ghoshal, A. and Renals, S.},
title = {{Noise compensation for subspace Gaussian mixture models}},
booktitle = {Proc. Interspeech},
year = {2012},
keywords = {acoustic modelling, noise compensation, SGMM, JUD},
abstract = {Joint uncertainty decoding (JUD) is an effective model-based noise compensation technique for conventional Gaussian mixture model (GMM) based speech recognition systems. In this paper, we apply JUD to subspace Gaussian mixture model (SGMM) based acoustic models. The total number of Gaussians in the SGMM acoustic model is usually much larger than for conventional GMMs, which limits the application of approaches which explicitly compensate each Gaussian, such as vector Taylor series (VTS). However, by clustering the Gaussian components into a number of regression classes, JUD-based noise compensation can be successfully applied to SGMM systems. We evaluate the JUD/SGMM technique using the Aurora 4 corpus, and the experimental results indicated that it is more accurate than conventional GMM-based systems using either VTS or JUD noise compensation.}
}
@inproceedings{lu_asru_2011,
author = {Lu, L. and Ghoshal, A. and Renals, S.},
title = {Regularized Subspace {G}ausian Mixture Models for Cross-lingual Speech Recognition},
booktitle = {Proc. ASRU},
year = {2011},
abstract = {We investigate cross-lingual acoustic modelling for low resource languages using the subspace Gaussian mixture model (SGMM). We assume the presence of acoustic models trained on multiple source languages, and use the global subspace parameters from those models for improved modelling in a target language with limited amounts of transcribed speech. Experiments on the GlobalPhone corpus using Spanish, Portuguese, and Swedish as source languages and German as target language (with 1 hour and 5 hours of transcribed audio) show that multilingually trained SGMM shared parameters result in lower word error rates (WERs) than using those from a single source language. We also show that regularizing the estimation of the SGMM state vectors by penalizing their $\ell_1$-norm help to overcome numerical instabilities and lead to lower WER.},
categories = {Subspace Gaussian Mixture Model, Cross-lingual, model regularization}
}
@inproceedings{Swietojanski:ASRU13,
author = {Swietojanski, P. and Ghoshal, A. and Renals, S.},
doi = {10.1109/ASRU.2013.6707744},
title = {HYBRID ACOUSTIC MODELS FOR DISTANT AND MULTICHANNEL LARGE VOCABULARY SPEECH RECOGNITION},
abstract = {We investigate the application of deep neural network (DNN)-hidden Markov model (HMM) hybrid acoustic models for far-ﬁeld speech recognition of meetings recorded using microphone arrays. We show that the hybrid models achieve significantly better accuracy than conventional systems based on Gaussian mixture models (GMMs). We observe up to 8% absolute word error rate (WER) reduction from a discriminatively trained GMM baseline when using a single distant microphone, and between 4–6% absolute WER reduction when using beamforming on various combinations of array channels. By training the networks on audio from multiple channels, we find the networks can recover significant part of accuracy difference between the single distant microphone and beamformed configurations. Finally, we show that the accuracy of a network recognising speech from a single distant microphone can approach that of a multi-microphone setup by training with data from other microphones.},
month = {December},
year = {2013},
booktitle = {Proc. IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
categories = {Distant Speech Recognition, Deep Neural Networks, Microphone Arrays, Beamforming, Meeting recognition}
}
@inproceedings{lu2013_nat,
author = {Lu, Liang and Ghoshal, Arnab and Renals, Steve},
title = {Noise adaptive training for subspace {Gaussian} mixture models},
abstract = {Noise adaptive training (NAT) is an effective approach to normalise environmental distortions when training a speech recogniser on noise-corrupted speech. This paper investigates the model-based NAT scheme using joint uncertainty decoding (JUD) for subspace Gaussian mixture models (SGMMs). A typical SGMM acoustic model has much larger number of surface Gaussian components, which makes it computationally infeasible to compensate each Gaussian explicitly. JUD tackles this problem by sharing the compensation parameters among the Gaussians and hence reduces the computational and memory demands. For noise adaptive training, JUD is reformulated into a generative model, which leads to an efficient expectation-maximisation (EM) based algorithm to update the SGMM acoustic model parameters. We evaluated the SGMMs with NAT on the Aurora 4 database, and obtained higher recognition accuracy compared to systems without adaptive training.},
year = {2013},
booktitle = {Proc. Interspeech},
categories = {adaptive training, noise robustness, joint uncertainty decoding, subspace Gaussian mixture models}
}
@inproceedings{lu2013_pronunciation,
author = {Lu, Liang and Ghoshal, Arnab and Renals, Steve},
doi = {10.1109/ASRU.2013.6707759},
title = {Acoustic Data-driven Pronunciation Lexicon for Large Vocabulary Speech Recognition},
abstract = {Speech recognition systems normally use handcrafted pronunciation lexicons designed by linguistic experts. Building and maintaining such a lexicon is expensive and time consuming. This paper concerns automatically learning a pronunciation lexicon for speech recognition. We assume the availability of a small seed lexicon and then learn the pronunciations of new words directly from speech that is transcribed at word-level. We present two implementations for refining the putative pronunciations of new words based on acoustic evidence. The first one is an expectation maximization (EM) algorithm based on weighted finite state transducers (WFSTs) and the other is its Viterbi approximation. We carried out experiments on the Switchboard corpus of conversational telephone speech. The expert lexicon has a size of more than 30,000 words, from which we randomly selected 5,000 words to form the seed lexicon. By using the proposed lexicon learning method, we have significantly improved the accuracy compared with a lexicon learned using a grapheme-to-phoneme transformation, and have obtained a word error rate that approaches that achieved using a fully handcrafted lexicon.},
year = {2013},
booktitle = {Proc. ASRU},
categories = {Lexical modelling, Probabilistic pronunciation model, Automatic speech recognition}
}
@article{lu2013cross,
author = {Lu, Liang and Ghoshal, Arnab and Renals, Steve},
doi = {10.1109/TASL.2013.2281575},
title = {{Cross-lingual subspace {Gaussian} mixture model for low-resource speech recognition}},
journal = {IEEE Transactions on Audio, Speech and Language Processing},
number = {1},
pages = {17--27},
volume = {22},
year = {2014},
abstract = {This paper studies cross-lingual acoustic modelling in the context of subspace Gaussian mixture models (SGMMs). SGMMs factorize the acoustic model parameters into a set that is globally shared between all the states of a hidden Markov model (HMM) and another that is specific to the HMM states. We demonstrate that the SGMM global parameters are transferable between languages, particularly when the parameters are trained multilingually. As a result, acoustic models may be trained using limited amounts of transcribed audio by borrowing the SGMM global parameters from one or more source languages, and only training the state-specific parameters on the target language audio. Model regularization using $\ell_1$-norm penalty is shown to be particularly effective at avoiding overtraining and leading to lower word error rates. We investigate maximum a posteriori (MAP) adaptation of subspace parameters in order to reduce the mismatch between the SGMM global parameters of the source and target languages. In addition, monolingual and cross-lingual speaker adaptive training is used to reduce the model variance introduced by speakers. We have systematically evaluated these techniques by experiments on the GlobalPhone corpus.},
categories = {acoustic modelling, subspace Gaussian mixture model, cross-lingual speech recognition, regularization, adaptation}
}
@inproceedings{Vesely:IS13,
author = {Vesely, Karel and Ghoshal, Arnab and Burget, Lukáš and Povey, Daniel},
title = {Sequence-discriminative training of deep neural networks},
booktitle = {Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech)},
month = {August},
year = {2013},
keywords = {myPubs, neural networks, discriminative training},
abstract = {Sequence-discriminative training of deep neural networks (DNNs) is investigated on a 300 hour American English conversational telephone speech task. Different sequence-discriminative criteria --- maximum mutual information (MMI), minimum phone error (MPE), state-level minimum Bayes risk (sMBR), and boosted MMI --- are compared. Two different heuristics are investigated to improve the performance of the DNNs trained using sequence-based criteria --- lattices are re-generated after the first iteration of training; and, for MMI and BMMI, the frames where the numerator and denominator hypotheses are disjoint are removed from the gradient computation. Starting from a competitive DNN baseline trained using cross-entropy, different sequence-discriminative criteria are shown to lower word error rates by 8-9% relative, on average. Little difference is noticed between the different sequence-based criteria that are investigated. The experiments are done using the open-source Kaldi toolkit, which makes it possible for the wider community to reproduce these results.}
}
@article{Swietojanski:SPL14,
author = {Swietojanski, P. and Ghoshal, A. and Renals, S.},
doi = {10.1109/LSP.2014.2325781},
title = {Convolutional Neural Networks for Distant Speech Recognition},
journal = {Signal Processing Letters, IEEE},
issn = {1070-9908},
number = {9},
abstract = {We investigate convolutional neural networks (CNNs) for large vocabulary distant speech recognition, trained using speech recorded from a single distant microphone (SDM) and multiple distant microphones (MDM). In the MDM case we explore a beamformed signal input representation compared with the direct use of multiple acoustic channels as a parallel input to the CNN. We have explored different weight sharing approaches, and propose a channel-wise convolution with two-way pooling. Our experiments, using the AMI meeting corpus, found that CNNs improve the word error rate (WER) by 6.5% relative compared to conventional deep neural network (DNN) models and 15.7% over a discriminatively trained Gaussian mixture model (GMM) baseline. For cross-channel CNN training, the WER improves by 3.5% relative over the comparable DNN structure. Compared with the best beamformed GMM system, cross-channel convolution reduces the WER by 9.7% relative, and matches the accuracy of a beamformed DNN.},
month = {September},
volume = {21},
year = {2014},
pages = {1120-1124},
categories = {distant speech recognition, deep neural networks, convolutional neural networks, meetings, AMI corpus}
}
@inproceedings{Aylett_Dall_Ghoshal_Henter_Merritt_Interspeech2014,
author = {Aylett, Matthew and Dall, Rasmus and Ghoshal, Arnab and Henter, Gustav Eje and Merritt, Thomas},
title = {A Flexible Front-End for {HTS}},
booktitle = {Proc. Interspeech},
abstract = {Parametric speech synthesis techniques depend on full context acoustic models generated by language front-ends, which analyse linguistic and phonetic structure. HTS, the leading parametric synthesis system, can use a number of different front-ends to generate full context models for synthesis and training. In this paper we explore the use of a new text processing front-end that has been added to the speech recognition toolkit Kaldi as part of an ongoing project to produce a new parametric speech synthesis system, Idlak. The use of XML specification files, a modular design, and modern coding and testing approaches, make the Idlak front-end ideal for adding, altering and experimenting with the contexts used in full context acoustic models. The Idlak front-end was evaluated against the standard Festival front-end in the HTS system. Results from the Idlak front-end compare well with the more mature Festival front-end (Idlak - 2.83 MOS vs Festival - 2.85 MOS), although a slight reduction in naturalness perceived by non-native English speakers can be attributed to Festival’s insertion of non-punctuated pauses.},
month = {September},
year = {2014},