Integrating Articulatory Features into HMM-based Parametric Speech Synthesis

This paper presents an investigation of ways to integrate articulatory features into Hidden Markov Model (HMM)-based parametric speech synthesis, primarily with the aim of improving the performance of acoustic parameter generation. The joint distribution of acoustic and articulatory features is estimated during training and is then used for parameter generation at synthesis time in conjunction with a maximum-likelihood criterion. Different model structures are explored to allow the articulatory features to influence acoustic modeling: model clustering, state synchrony and cross-stream feature dependency. The results of objective evaluation show that the accuracy of acoustic parameter prediction can be improved when shared clustering and asynchronous-state model structures are adopted for combined acoustic and articulatory features. More significantly, our experiments demonstrate that modeling the dependency between these two feature streams can make speech synthesis more flexible. The characteristics of synthetic speech can be easily controlled by modifying generated articulatory features as part of the process of acoustic parameter generation

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

Graphical models for social behavior modeling in face-to face interaction

International audienceThe goal of this paper is to model the coverbal behavior of a subject involved in face-to-face social interactions. For this end, we present a multimodal behavioral model based on a Dynamic Bayesian Network (DBN). The model was inferred from multimodal data of interacting dyads in a specific scenario designed to foster mutual attention and multimodal deixis of objects and places in a collaborative task. The challenge for this behavioral model is to generate coverbal actions (gaze, hand gestures) for the subject given his verbal productions, the current phase of the interaction and the perceived actions of the partner. In our work, the structure of the DBN was learned from data, which revealed an interesting causality graph describing precisely how verbal and coverbal human behaviors are coordinated during the studied interactions. Using this structure, DBN exhibits better performances compared to classical baseline models such as Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs). We outperform the baseline in both measures of performance, i.e. interaction unit recognition and behavior generation. DBN also reproduces more faithfully the coordination patterns between modalities observed in ground truth compared to the baseline models

Statistical text-to-speech synthesis of Spanish subtitles

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13623-3_5Online multimedia repositories are growing rapidly. However, language barriers are often difficult to overcome for many of the current and potential users. In this paper we describe a TTS Spanish sys- tem and we apply it to the synthesis of transcribed and translated video lectures. A statistical parametric speech synthesis system, in which the acoustic mapping is performed with either HMM-based or DNN-based acoustic models, has been developed. To the best of our knowledge, this is the first time that a DNN-based TTS system has been implemented for the synthesis of Spanish. A comparative objective evaluation between both models has been carried out. Our results show that DNN-based systems can reconstruct speech waveforms more accurately.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755 (transLectures) and ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme (CIP) under grant agreement no 621030 (EMMA), and the Spanish MINECO Active2Trans (TIN2012-31723) research project.Piqueras Gozalbes, SR.; Del Agua Teba, MA.; Giménez Pastor, A.; Civera Saiz, J.; Juan Císcar, A. (2014). Statistical text-to-speech synthesis of Spanish subtitles. En Advances in Speech and Language Technologies for Iberian Languages: Second International Conference, IberSPEECH 2014, Las Palmas de Gran Canaria, Spain, November 19-21, 2014. Proceedings. Springer International Publishing. 40-48. https://doi.org/10.1007/978-3-319-13623-3_5S4048Ahocoder, http://aholab.ehu.es/ahocoderCoursera, http://www.coursera.orgHMM-Based Speech Synthesis System (HTS), http://hts.sp.nitech.ac.jpKhan Academy, http://www.khanacademy.orgAxelrod, A., He, X., Gao, J.: Domain adaptation via pseudo in-domain data selection. In: Proc. of EMNLP, pp. 355–362 (2011)Bottou, L.: Stochastic gradient learning in neural networks. In: Proceedings of Neuro-Nîmes 1991. EC2, Nimes, France (1991)Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on Audio, Speech, and Language Processing 20(1), 30–42 (2012)Erro, D., Sainz, I., Navas, E., Hernaez, I.: Harmonics plus noise model based vocoder for statistical parametric speech synthesis. IEEE Journal of Selected Topics in Signal Processing 8(2), 184–194 (2014)Fan, Y., Qian, Y., Xie, F., Soong, F.: TTS synthesis with bidirectional LSTM based recurrent neural networks. In: Proc. of Interspeech (submitted 2014)Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29(6), 82–97 (2012)Hunt, A.J., Black, A.W.: Unit selection in a concatenative speech synthesis system using a large speech database. In: Proc. of ICASSP, vol. 1, pp. 373–376 (1996)King, S.: Measuring a decade of progress in text-to-speech. Loquens 1(1), e006 (2014)Koehn, P.: Statistical Machine Translation. Cambridge University Press (2010)Kominek, J., Schultz, T., Black, A.W.: Synthesizer voice quality of new languages calibrated with mean mel cepstral distortion. In: Proc. of SLTU, pp. 63–68 (2008)Lopez, A.: Statistical machine translation. ACM Computing Surveys 40(3), 8:1–8:49 (2008)poliMedia: The polimedia video-lecture repository (2007), http://media.upv.esSainz, I., Erro, D., Navas, E., Hernáez, I., Sánchez, J., Saratxaga, I.: Aholab speech synthesizer for albayzin 2012 speech synthesis evaluation. In: Proc. of IberSPEECH, pp. 645–652 (2012)Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in context-dependent dnn for conversational speech transcription. In: Proc. of ASRU, pp. 24–29 (2011)Shinoda, K., Watanabe, T.: MDL-based context-dependent subword modeling for speech recognition. Journal of the Acoustical Society of Japan 21(2), 79–86 (2000)Silvestre-Cerdà, J.A., et al.: Translectures. In: Proc. of IberSPEECH, pp. 345–351 (2012)TED Ideas worth spreading, http://www.ted.comThe transLectures-UPV Team.: The transLectures-UPV toolkit (TLK), http://translectures.eu/tlkToda, T., Black, A.W., Tokuda, K.: Mapping from articulatory movements to vocal tract spectrum with Gaussian mixture model for articulatory speech synthesis. In: Proc. of ISCA Speech Synthesis Workshop (2004)Tokuda, K., Kobayashi, T., Imai, S.: Speech parameter generation from hmm using dynamic features. In: Proc. of ICASSP, vol. 1, pp. 660–663 (1995)Tokuda, K., Masuko, T., Miyazaki, N., Kobayashi, T.: Multi-space probability distribution HMM. IEICE Transactions on Information and Systems 85(3), 455–464 (2002)transLectures: D3.1.2: Second report on massive adaptation, http://www.translectures.eu/wp-content/uploads/2014/01/transLectures-D3.1.2-15Nov2013.pdfTurró, C., Ferrando, M., Busquets, J., Cañero, A.: Polimedia: a system for successful video e-learning. In: Proc. of EUNIS (2009)Videolectures.NET: Exchange ideas and share knowledge, http://www.videolectures.netWu, Y.J., King, S., Tokuda, K.: Cross-lingual speaker adaptation for HMM-based speech synthesis. In: Proc. of ISCSLP, pp. 1–4 (2008)Yamagishi, J.: An introduction to HMM-based speech synthesis. Tech. rep. Centre for Speech Technology Research (2006), https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/TrajectoryModelling/HTS-Introduction.pdfYoshimura, T., Tokuda, K., Masuko, T., Kobayashi, T., Kitamura, T.: Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis. In: Proc. of Eurospeech, pp. 2347–2350 (1999)Zen, H., Senior, A.: Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis. In: Proc. of ICASSP, pp. 3872–3876 (2014)Zen, H., Senior, A., Schuster, M.: Statistical parametric speech synthesis using deep neural networks. In: Proc. of ICASSP, pp. 7962–7966 (2013)Zen, H., Tokuda, K., Black, A.W.: Statistical parametric speech synthesis. Speech Communication 51(11), 1039–1064 (2009