Publications by Olga Goubanova

s9808417.bib

@inproceedings{Goubanova:2003,
  author = {Goubanova, O.},
  ps = {http://www.cstr.ed.ac.uk/downloads/publications/2003/OGoubanova_icphs2k3.ps},
  title = {{B}ayesian Modelling Of Vowel Segment Duration For Text-to-Speech Synthesis Using Distinctive Features},
  booktitle = {Proc. ICPhS 2003},
  year = {2003},
  abstract = {We report the results of applying the Bayesian Belief Network (BN) approach to predicting vowel duration. A Bayesian inference of the vowel duration is performed on a hybrid Bayesian network consisting of discrete and continuous nodes, with the nodes in the network representing the linguistic factors that affect segment duration. New to the present research, we model segment identity factor as a set of distinctive features. The features chosen were height, frontness, length, and roundness. We also experimented with a word class feature that implicitly represents word frequency information. We contrasted the results of the belief network model with those of the sums of products (SoP) model and classification and regression tree (CART) model. We trained and tested all three models on the same data. In terms of the RMS error and correlation coefficient, our BN model performs no worse than SoP model, and it significantly outperforms CART model.},
  volume = {3},
  address = {Barcelona, Spain},
  pages = {2349},
  categories = {Bayesian, text-to-speech synthesis, duration modelling}
}

@inproceedings{Goubanova:2001,
  author = {Goubanova, O.},
  booktitle = {CD-ROM Proc. 4th ISCA Tutorial and Research Workshop on Speech Synthesis},
  year = {2001},
  address = {Scotland, UK},
  title = {Predicting segmental durations using {B}ayesian {B}elief Networks}
}

@article{goubanova:king:specom2008,
  author = {Goubanova, Olga and King, Simon},
  doi = {10.1016/j.specom.2007.10.002},
  title = {Bayesian networks for phone duration prediction},
  journal = {Speech Communication},
  number = {4},
  abstract = {In a text-to-speech system, the duration of each phone may be predicted by a duration model. This model is usually trained using a database of phones with known durations; each phone (and the context it appears in) is characterised by a feature vector that is composed of a set of linguistic factor values. We describe the use of a graphical model -- a Bayesian network -- for predicting the duration of a phone, given the values for these factors. The network has one discrete variable for each of the linguistic factors and a single continuous variable for the phone's duration. Dependencies between variables (or the lack of them) are represented in the BN structure by arcs (or missing arcs) between pairs of nodes. During training, both the topology of the network and its parameters are learned from labelled data. We compare the results of the BN model with results for sums of products and CART models on the same data. In terms of the root mean square error, the BN model performs much better than both CART and SoP models. In terms of correlation coefficient, the BN model performs better than the SoP model, and as well as the CART model. A BN model has certain advantages over CART and SoP models. Training SoP models requires a high degree of expertise. CART models do not deal with interactions between factors in any explicit way. As we demonstrate, a BN model can also make accurate predictions of a phone's duration, even when the values for some of the linguistic factors are unknown.},
  month = {April},
  volume = {50},
  year = {2008},
  pages = {301-311},
  categories = {Text-to-speech; Bayesian networks; Duration modelling; Sums of products; Classification and regression trees}
}

@inproceedings{Goubanova:2002,
  author = {Goubanova, O.},
  booktitle = {Proc. ICSLP 2002},
  year = {2002},
  address = {Denver, USA},
  title = {Forms of Introduction in Map Task Dialogues: Case of {L2} {Russian} Speakers}
}

@inproceedings{Goubanova-Taylor:2000,
  author = {Goubanova, O. and Taylor, P.},
  booktitle = {CD-ROM Proc. ICSLP 2000},
  year = {2000},
  address = {Beijing, China},
  title = {Using {B}ayesian {B}elief Networks for model duration in text-to-speech systems}
}

@inproceedings{goubanova_king_isp05,
  author = {Goubanova, Olga and King, Simon},
  title = {Predicting Consonant Duration with {B}ayesian Belief Networks},
  booktitle = {Proc. Interspeech 2005},
  year = {2005},
  address = {Lisbon, Portugal},
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2005/goubanova_king_isp2005.pdf},
  abstract = {Consonant duration is influenced by a number of linguistic factors such as the consonant s identity, within-word position, stress level of the previous and following vowels, phrasal position of the word containing the target consonant, its syllabic position, identity of the previous and following segments. In our work, consonant duration is predicted from a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the consonant s duration. Interactions between factors are represented as conditional dependency arcs in this graphical model. Given the parameters of the belief network, the duration of each consonant in the test set is then predicted as the value with the maximum probability. We compare the results of the belief network model with those of sums-of-products (SoP) and classification and regression tree (CART) models using the same data. In terms of RMS error, our BN model performs better than both CART and SoP models. In terms of the correlation coefficient, our BN model performs better than SoP model, and no worse than CART model. In addition, the Bayesian model reliably predicts consonant duration in cases of missing or hidden linguistic factors.}
}