Improvements on automatic speech segmentation at the phonetic level

In this paper, we present some recent improvements in our automatic speech segmentation system, which only needs the speech signal and the phonetic sequence of each sentence of a corpus to be trained. It estimates a GMM by using all the sentences of the training subcorpus, where each Gaussian distribution represents an acoustic class, which probability densities are combined with a set of conditional probabilities in order to estimate the probability densities of the states of each phonetic unit. The initial values of the conditional probabilities are obtained by using a segmentation of each sentence assigning the same number of frames to each phonetic unit. A DTW algorithm fixes the phonetic boundaries using the known phonetic sequence. This DTW is a step inside an iterative process which aims to segment the corpus and re-estimate the conditional probabilities. The results presented here demonstrate that the system has a good capacity to learn how to identify the phonetic boundaries. © 2011 Springer-Verlag.This work was supported by the Spanish MICINN under contract TIN2008-06856-C05-02Gómez Adrian, JA.; Calvo Lance, M. (2011). Improvements on automatic speech segmentation at the phonetic level. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Verlag (Germany). 7042:557-564. https://doi.org/10.1007/978-3-642-25085-9_66S5575647042Toledano, D.T., Hernández Gómez, L., Villarrubia Grande, L.: Automatic Phonetic Segmentation. IEEE Transactions on Speech and Audio Processing 11(6), 617–625 (2003)Kipp, A., Wesenick, M.B., Schiel, F.: Pronunciation modelling applied to automatic segmentation of spontaneous speech. In: Proceedings of Eurospeech, Rhodes, Greece, pp. 2013–2026 (1997)Sethy, A., Narayanan, S.: Refined Speech Segmentation for Concatenative Speech Synthesis. In: Proceedings of ICSLP, Denver, Colorado, USA, pp. 149–152 (2002)Jarify, S., Pastor, D., Rosec, O.: Cooperation between global and local methods for the automatic segmentation of speech synthesis corpora. In: Proceedings of Interspeech, Pittsburgh, Pennsylvania, USA, pp. 1666–1669 (2006)Romsdorfer, H., Pfister, B.: Phonetic Labeling and Segmentation of Mixed-Lingual Prosody Databases. In: Proceedings of Interspeech, Lisbon, Portual, pp. 3281–3284 (2005)Paulo, S., Oliveira, L.C.: DTW-based Phonetic Alignment Using Multiple Acoustic Features. In: Proceedings of Eurospeech, Geneva, Switzerland, pp. 309–312 (2003)Park, S.S., Shin, J.W., Kim, N.S.: Automatic Speech Segmentation with Multiple Statistical Models. In: Proceedings of Interspeech, Pittsburgh, Pennsylvania, USA, pp. 2066–2069 (2006)Mporas, I., Ganchev, T., Fakotakis, N.: Speech segmentation using regression fusion of boundary predictions. Computer Speech and Language 24, 273–288 (2010)Povey, D., Woodland, P.C.: Minimum Phone Error and I-smoothing for improved discriminative training. In: Proceedings of ICASSP, Orlando, Florida, USA, pp. 105–108 (2002)Kuo, J.W., Wang, H.M.: Minimum Boundary Error Training for Automatic Phonetic Segmentation. In: Proceedings of Interspeech, Pittsburgh, Pennsylvania, USA, pp. 1217–1220 (2006)Huggins-Daines, D., Rudnicky, A.I.: A Constrained Baum-Welch Algorithm for Improved Phoneme Segmentation and Efficient Training. In: Proceedings of Interspeech, Pittsburgh, Pennsylvania, USA, pp. 1205–1208 (2006)Ogbureke, K.U., Carson-Berndsen, J.: Improving initial boundary estimation for HMM-based automatic phonetic segmentation. In: Proceedings of Interspeech, Brighton, UK, pp. 884–887 (2009)Gómez, J.A., Castro, M.J.: Automatic Segmentation of Speech at the Phonetic Level. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 672–680. Springer, Heidelberg (2002)Gómez, J.A., Sanchis, E., Castro-Bleda, M.J.: Automatic Speech Segmentation Based on Acoustical Clustering. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR&SPR 2010. LNCS, vol. 6218, pp. 540–548. Springer, Heidelberg (2010)Moreno, A., Poch, D., Bonafonte, A., Lleida, E., Llisterri, J., Mariño, J.B., Nadeu, C.: Albayzin Speech Database: Design of the Phonetic Corpus. In: Proceedings of Eurospeech, Berlin, Germany, vol. 1, pp. 653–656 (September 1993)TIMIT Acoustic-Phonetic Continuous Speech Corpus, National Institute of Standards and Technology Speech Disc 1-1.1, NTIS Order No. PB91-5050651996 (October 1990

Prosody-Based Automatic Segmentation of Speech into Sentences and Topics

A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken language. We investigate the use of prosody (information gleaned from the timing and melody of speech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with word-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Results show that the prosodic model alone performs on par with, or better than, word-based statistical language models -- for both true and automatically recognized words in news speech. The prosodic model achieves comparable performance with significantly less training data, and requires no hand-labeling of prosodic events. Across tasks and corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent boundary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pitch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2), Special Issue on Accessing Information in Spoken Audio, September 200

BEA – A multifunctional Hungarian spoken language database

In diverse areas of linguistics, the demand for studying actual language use is on the increase. The aim of developing a phonetically-based multi-purpose database of Hungarian spontaneous speech, dubbed BEA2, is to accumulate a large amount of spontaneous speech of various types together with sentence repetition and reading. Presently, the recorded material of BEA amounts to 260 hours produced by 280 present-day Budapest speakers (ages between 20 and 90, 168 females and 112 males), providing also annotated materials for various types of research and practical applications

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure