Abstract
The prosody of the speech signal conveys information over the linguistic content of the message: prosody structures the utterance, and also brings information on speaker’s attitude and speaker’s emotion. Duration of sounds, energy and fundamental frequency are the prosodic features. However their automatic computation and usage are not obvious. Sound duration features are usually extracted from speech recognition results or from a force speech-text alignment. Although the resulting segmentation is usually acceptable on clean native speech data, performance degrades on noisy or not non-native speech. Many algorithms have been developed for computing the fundamental frequency, they lead to rather good performance on clean speech, but again, performance degrades in noisy conditions. However, in some applications, as for example in computer assisted language learning, the relevance of the prosodic features is critical; indeed, the quality of the diagnostic on the learner’s pronunciation will heavily depend on the precision and reliability of the estimated prosodic parameters. The paper considers the computation of prosodic features, shows the limitations of automatic approaches, and discusses the problem of computing confidence measures on such features. Then the paper discusses the role of prosodic features and how they can be handled for automatic processing in some tasks such as the detection of discourse particles, the characterization of emotions, the classification of sentence modalities, as well as in computer assisted language learning and in expressive speech synthesis.
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ORFEO project: http://www.projet-orfeo.fr/.
References
Bartkova, K., Dargnat, M., Jouvet, D., Lee, L.: Annotation of discourse particles in French over a large variety of speech corpora. In: ACor4French - Les corpus annotés du français, TALN 2017 - Traitement Automatique des Langues Naturelles. Orléans, France, June 2017. https://hal.inria.fr/hal-01585540
Bartkova, K., Jouvet, D.: Automatic detection of the prosodic structures of speech utterances. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS (LNAI), vol. 8113, pp. 1–8. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-01931-4_1
Bartkova, K., Jouvet, D.: Links between manual punctuation marks and automatically detected prosodic structures. In: Speech Prosody 2014, Dublin, Ireland, May 2014. https://hal.archives-ouvertes.fr/hal-00998031
Bartkova, K., Jouvet, D.: Analysis of prosodic correlates of emotional speech data. In: 9th Tutorial and Research Workshop on Experimental Linguistics, ExLing 2018, Paris, France, August 2018. https://hal.inria.fr/hal-01889932
Bartkova, K., Jouvet, D., Delais-Roussarie, E.: Prosodic parameters and prosodic structures of French emotional data. In: Speech Prosody 2016, Boston, USA, May 2016. https://hal.inria.fr/hal-01293516
Benzeghiba, M., et al.: Automatic speech recognition and speech variability: a review. Speech Commun. 49, 763–786 (2007). https://hal.inria.fr/inria-00616506
Bisani, M., Ney, H.: Joint-sequence models for grapheme-to-phoneme conversion. Speech Commun. 50(5), 434–451 (2008)
Boersma, P.: Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In: Proceedings of the Institute of Phonetic Sciences, Amsterdam, vol. 17, pp. 97–110 (1993)
Boersma, P., Weenink, D.: Praat: doing phonetics by computer [computer program]. Version 6.0.20 (2011)
Bonneau, A., et al.: Gestion d’erreurs pour la fiabilisation des retours automatiques en apprentissage de la prosodie d’une langue seconde. Traitement Automatique des Langues 53(3) (2013). https://hal.inria.fr/hal-00834278
Camacho, A., Harris, J.G.: A sawtooth waveform inspired pitch estimator for speech and music. J. Acoust. Soc. Am. 124(3), 1638–1652 (2008)
de Cheveigné, A., Kawahara, H.: YIN, a fundamental frequency estimator for speech and music. J. Acoust. Soc. Am. 111(4), 1917–1930 (2002)
Dargnat, M., Bartkova, K., Jouvet, D.: Discourse particles in French: prosodic parameters extraction and analysis. In: Dediu, A.-H., Martín-Vide, C., Vicsi, K. (eds.) SLSP 2015. LNCS (LNAI), vol. 9449, pp. 39–49. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25789-1_5. https://hal.inria.fr/hal-01184197
Deng, B., Jouvet, D., Laprie, Y., Steiner, I., Sini, A.: Towards confidence measures on fundamental frequency estimations. In: IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, USA, March 2017. https://hal.inria.fr/hal-01493168
Eskenazi, M.: An overview of spoken language technology for education. Speech Commun. 51(10), 832–844 (2009)
European Language Resources Association (ELRA): Speecon manually pitch-marked reference database for Spanish, ISLRN : 866–498-919-979-7, ELRA ID: ELRA-S0218, Catalogue ELRA. (http://catalog.elra.info/)
Fohr, D., Mella, O., Jouvet, D.: De l’importance de l’homogénéisation des conventions de transcription pour l’alignement automatique de corpus oraux de parole spontanée. In: 8es Journées Internationales de Linguistique de Corpus (JLC2015). Orléans, France, September 2015. https://hal.inria.fr/hal-01183352
Ghahremani, P., BabaAli, B., Povey, D., Riedhammer, K., Trmal, J., Khudanpur, S.: A pitch extraction algorithm tuned for automatic speech recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 2494–2498 (2014)
Illina, I., Fohr, D., Jouvet, D.: Multiple pronunciation generation using grapheme-to-phoneme conversion based on conditional random fields. In: XIV International Conference “Speech and Computer” (SPECOM 2011), Kazan, Russia, September 2011. https://hal.inria.fr/inria-00616325
Illina, I., Fohr, D., Jouvet, D.: Génération des prononciations de noms propres à l’aide des champs aéatoires conditionnels. In: JEP-TALN-RECITAL 2012, Grenoble, France, June 2012. https://hal.inria.fr/hal-00753381
Jouvet, D., Bartkova, K.: Acoustical frame rate and pronunciation variant statistics. In: Dediu, A.-H., Martín-Vide, C., Vicsi, K. (eds.) SLSP 2015. LNCS (LNAI), vol. 9449, pp. 123–134. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25789-1_12. https://hal.inria.fr/hal-01184195
Jouvet, D., Bartkova, K., Dargnat, M., Lee, L.: Analysis and automatic classification of some discourse particles on a large set of french spoken corpora. In: 5th International Conference on Statistical Language and Speech Processing, SLSP 2017, Le Mans, France, October 2017. https://hal.inria.fr/hal-01585567
Jouvet, D., Laprie, Y.: Performance analysis of several pitch detection algorithms on simulated and real noisy speech data. In: 25th European Signal Processing Conference, EUSIPCO 2017, Kos, Greece, August 2017. https://hal.inria.fr/hal-01585554
Jouvet, D., Mesbahi, L., Bonneau, A., Fohr, D., Illina, I., Laprie, Y.: Impact of pronunciation variant frequency on automatic non-native speech segmentation. In: 5th Language and Technology Conference - LTC 2011, Poznan, Poland, pp. 145–148, November 2011. https://hal.archives-ouvertes.fr/hal-00639118
Jurafsky, D., et al.: Automatic detection of discourse structure for speech recognition and understanding. In: 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings, pp. 88–95. IEEE (1997)
Kawahara, H., de Cheveigné, A., Banno, H., Takahashi, T., Irino, T.: Nearly defect-free F0 trajectory extraction for expressive speech modifications based on STRAIGHT. In: Interspeech, pp. 537–540 (2005)
Kawahara, H., Estill, J., Fujimura, O.: Aperiodicity extraction and control using mixed mode excitation and group delay manipulation for a high quality speech analysis, modification and synthesis system straight. In: MAVEBA, pp. 59–64 (2001)
Kawahara, H., Katayose, H., De Cheveigné, A., Patterson, R.D.: Fixed point analysis of frequency to instantaneous frequency mapping for accurate estimation of F0 and periodicity. In: Eurospeech, pp. 2781–2784 (1999)
Kolář, J., Lamel, L.: Development and evaluation of automatic punctuation for French and English speech-to-text. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)
Král, P., Kleckova, J., Cerisara, C.: Sentence modality recognition in French based on prosody. In: International Conference on Enformatika, Systems Sciences and Engineering-ESSE, vol. 8, pp. 185–188. Citeseer (2005)
Kulkarni, A., Vincent, C., Denis, J.: Layer adaptation for transfer of expressivity in speech synthesis. In: 9th Language and Technology Conference Proceedings of LTC 2019 (2019)
Lanjewar, R.B., Chaudhari, D.: Speech emotion recognition: a review. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2, 68–71 (2013)
Lee, L., Bartkova, K., Dargnat, M., Jouvet, D.: Prosodic and pragmatic values of discourse particles in French. In: 9th Tutorial and Research Workshop on Experimental Linguistics, ExLing 2018, Paris, France, August 2018. https://hal.inria.fr/hal-01889925
Lee, L., Bartkova, K., Jouvet, D., Dargnat, M., Yvon, K.: Can prosody meet pragmatics? Case of discourse particles in French. In: To Appear in Proceedings of ICPhS 2019. International Congress of Phonetic Sciences (2019)
Margolis, A., Ostendorf, M.: Question detection in spoken conversations using textual conversations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2, pp. 118–124. Association for Computational Linguistics (2011)
Martin, P.: Prosodic and rhythmic structures in French. Linguistics 25(5), 925–950 (1987)
Mesbahi, L., Jouvet, D., Bonneau, A., Fohr, D., Illina, I., Laprie, Y.: Reliability of non-native speech automatic segmentation for prosodic feedback. In: Workshop on Speech and Language Technology in Education - SLaTE 2011. ISCA, Venise, August 2011. https://hal.inria.fr/inria-00614930
Novak, J.R., Minematsu, N., Hirose, K.: Phonetisaurus: exploring grapheme-to-phoneme conversion with joint n-gram models in the WFST framework. Nat. Lang. Eng. 22(6), 907–938 (2016)
Orosanu, L., Jouvet, D.: Combining lexical and prosodic features for automatic detection of sentence modality in French. In: Dediu, A.-H., Martín-Vide, C., Vicsi, K. (eds.) SLSP 2015. LNCS (LNAI), vol. 9449, pp. 207–218. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25789-1_20. https://hal.inria.fr/hal-01184196
Orosanu, L., Jouvet, D., Fohr, D., Illina, I., Bonneau, A.: Combining criteria for the detection of incorrect entries of non-native speech in the context of foreign language learning. In: SLT 2012–4th IEEE Workshop on Spoken Language Technology, Miami, United States, December 2012. https://hal.inria.fr/hal-00753458
Pirker, G., Wohlmayr, M., Petrik, S., Pernkopf, F.: A pitch tracking corpus with evaluation on multipitch tracking scenario. In: Interspeech, pp. 1509–1512 (2011)
Quang, V.M., Castelli, E., Yên, P.N.: A decision tree-based method for speech processing: question sentence detection. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 1205–1212. Springer, Heidelberg (2006). https://doi.org/10.1007/11881599_150
Rao, K., Peng, F., Sak, H., Beaufays, F.: Grapheme-to-phoneme conversion using long short-term memory recurrent neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4225–4229. IEEE (2015)
Schröder, M.: Expressive speech synthesis: past, present, and possible futures. In: Tao, J., Tan, T. (eds.) Affective Information Processing, pp. 111–126. Springer, London (2009). https://doi.org/10.1007/978-1-84800-306-4_7
Schuller, B.W.: Speech emotion recognition: two decades in a nutshell, benchmarks, and ongoing trends. Commun. ACM 61(5), 90–99 (2018)
Segal, N., Bartkova, K.: Prosodic structure representation for boundary detection in spontaneous French. In: Proceedings of ICPhS, pp. 1197–1200 (2007)
Sethu, V., Epps, J., Ambikairajah, E.: Speech based emotion recognition. In: Ogunfunmi, T., Togneri, R., Narasimha, M.S. (eds.) Speech and Audio Processing for Coding, Enhancement and Recognition, pp. 197–228. Springer, New York (2015). https://doi.org/10.1007/978-1-4939-1456-2_7
Shadiev, R., Hwang, W.Y., Huang, Y.M.: Review of research on mobile language learning in authentic environments. Comput. Assist. Lang. Learn. 30(3–4), 284–303 (2017)
Sorin, A., et al.: The ETSI extended distributed speech recognition (DSR) standards: client side processing and tonal language recognition evaluation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 129–132 (2004)
Stede, M., Schmitz, B.: Discourse particles and discourse functions. Mach. Transl. 15(1–2), 125–147 (2000)
Strömbergsson, S.: Today’s most frequently used f 0 estimation methods, and their accuracy in estimating male and female pitch in clean speech. In: Interspeech 2016, pp. 525–529 (2016)
Talkin, D.: A robust algorithm for pitch tracking (RAPT). In: Kleijn, W.B., Paliwal, K.K. (eds.) Speech Coding and Synthesis, pp. 495–518. Elsevier, Amsterdam (1995)
Viberg, O., Grönlund, Å.: Mobile assisted language learning: a literature review. In: 11th World Conference on Mobile and Contextual Learning (2012)
Witt, S.M., Young, S.J.: Phone-level pronunciation scoring and assessment for interactive language learning. Speech Commun. 30(2–3), 95–108 (2000)
Wu, Z., Watts, O., King, S.: Merlin: an open source neural network speech synthesis system. In: SSW, pp. 202–207 (2016)
Yao, K., Zweig, G.: Sequence-to-sequence neural net models for grapheme-to-phoneme conversion. arXiv preprint arXiv:1506.00196 (2015)
Zen, H., et al.: The HMM-based speech synthesis system (HTS) version 2.0. In: SSW, pp. 294–299. Citeseer (2007)
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Jouvet, D. (2019). Speech Processing and Prosody. In: Ekštein, K. (eds) Text, Speech, and Dialogue. TSD 2019. Lecture Notes in Computer Science(), vol 11697. Springer, Cham. https://doi.org/10.1007/978-3-030-27947-9_1
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