Abstract
We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data. We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter). We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR). We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features. Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement.
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Stolcke, A., Shriberg, E., Bates, R., Ostendorf, M., Hakkani, D., Plauche, M., Tur, G., Lu, Y.: Automatic Detection of Sentence Boundaries and Disfluencies Based on Recognized Words. In: Proc. ICSLP 1998, Sydney, pp. 2247–2250 (1998)
Shriberg, E., Stolcke, A., Hakkani-Tur, D., Tur, G.: Prosody-based Automatic Segmentation of Speech into Sentences and Topics. Speech Communication 32(1-2), 127–154 (2000)
Warnke, V., Kompe, R., Niemann, H., Nöth, E.: Integrated Dialog Act Segmentation and Classification Using Prosodic Features and Language Models. In: Proc. EUROSPEECH 1997, Rhodes, Greece, pp. 207–210 (1997)
Huang, J., Zweig, G.: Maximum Entropy Model for Punctuation Annotation from Speech. In: Proc. ICSLP 2002, Denver, pp. 917–920 (2002)
Kim, J.H., Woodland, P.: A Combined Punctuation Generation and Speech Recognition System and Its Performance Enhancement Using Prosody. Speech Communication 41(4), 563–577 (2003)
Liu, Y., Stolcke, A., Harper, M., Shriberg, E.: Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech. In: Proc. EMNLP, Barcelona, Spain (2004)
Liu, Y., Shriberg, E., Stolcke, A., Hillard, D., Ostendorf, M., Peskin, B., Harper, M.: The ICSI-SRI-UW Metadata Extraction System. In: ICSLP 2004, Jeju, Korea (2004)
Kolář, J., Švec, J., Psutka, J.: Automatic Punctuation Annotation in Czech Broadcast News Speech. In: Proc. SPECOM 2004, St. Petersburg, Russia (2004)
Liu, Y., Stolcke, A., Shriberg, E., Harper, M.: Using Conditional Random Fields for Sentence Boundary Detection in Speech. In: Proc. ACL, Ann Arbor, pp. 451–458 (2005)
Ang, J., Liu, Y., Shriberg, E.: Automatic Dialog Act Segmentation and Classification in Multiparty Meetings. In: Proc. IEEE ICASSP 2005, Philadelphia, pp. 1061–1064 (2005)
Ji, G., Bilmes, J.: Dialog Act Tagging Using Graphical Models. In: Proc. IEEE ICASSP 2005, Philadelphia, pp. 33–36 (2005)
Zimmermann, M., Stolcke, A., Shriberg, E.: Joint Segmentation and Classification of Dialog Acts in Multiparty Meetings. In: Proc.: IEEE ICASSP 2006, Toulouse, France (2006)
Janin, A., Baron, D., Edwards, J., Ellis, D., Gelbart, D., Morgan, N., Peskin, B., Pfau, T., Shriberg, E., Stolcke, A., Wooters, C.: The ICSI Meeting Corpus. In: Proc. IEEE ICASSP 2003, Hong Kong, pp. 364–367 (2003)
Dhillon, R., et al.: Meeting Recorder Project: Dialog Act Labeling Guide. ICSI Technical Report TR-04-02, International Computer Science Institute, Berkeley (2004)
Shriberg, E., et al.: The ICSI Meeting Recorder Dialog Act (MRDA) Corpus. In: Proc. SIGDIAL, Cambridge, MA, USA (2004)
Zhu, Q., Stolcke, A., Chen, B., Morgan, N.: Using MLP Features in SRI’s Conversational Speech Recognition System. In: Proc. INTERSPEECH 2005, Lisboa, pp. 2141–2144 (2005)
Buckow, J., Warnke, V., Huber, R., Batliner, A., Nöth, E., Niemann, H.: Fast and Robust Features for Prosodic Classification. In: Matoušek, V., Mautner, P., Ocelíková, J., Sojka, P. (eds.) TSD 1999. LNCS (LNAI), vol. 1692, pp. 193–198. Springer, Heidelberg (1999)
Liu, Y., Shriberg, E., Stolcke, A., Harper, M.: Using Machine Learning to Cope with Imbalanced Classes in Natural Speech: Evidence from Sentence Boundary and Disfluency Detection. In: Proc ICSLP 2004, Jeju, Korea (2004)
Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)
Schapire, R.E., Singer, Y.: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning 39(2/3), 135–168 (2000)
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Kolář, J., Shriberg, E., Liu, Y. (2006). Using Prosody for Automatic Sentence Segmentation of Multi-party Meetings. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2006. Lecture Notes in Computer Science(), vol 4188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11846406_79
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DOI: https://doi.org/10.1007/11846406_79
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