Advertisement

Lexical Emphasis Detection in Spoken French Using F-BANKs and Neural Networks

  • Abdelwahab Heba
  • Thomas Pellegrini
  • Tom Jorquera
  • Régine André-Obrecht
  • Jean-Pierre Lorré
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10583)

Abstract

Expressiveness and non-verbal information in speech are active research topics in speech processing. In this work, we are interested in detecting emphasis at word-level as a mean to identify what are the focus words in a given utterance. We compare several machine learning techniques (Linear Discriminant Analysis, Support Vector Machines, Neural Networks) for this task carried out on SIWIS, a French speech synthesis database. Our approach consists first in aligning the spoken words to the speech signal and second to feed classifier with filter bank coefficients in order to take a binary decision at word-level: neutral/emphasized. Evaluation results show that a three-layer neural network performed best with a \(93\%\) accuracy.

Keywords

Emphasized content recognition Non verbal information in speech SIWIS French speech synthesis database 

References

  1. 1.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
  2. 2.
    Bisani, M., Ney, H.: Joint-sequence models for grapheme-to-phoneme conversion. Speech Commun. 50(5), 434–451 (2008)CrossRefGoogle Scholar
  3. 3.
    Campbell, N.: Loudness, spectral tilt, and perceived prominence in dialogues. In: Proceedings ICPhS, vol. 95, pp. 676–679 (1995)Google Scholar
  4. 4.
    Campbell, N.: On the use of nonverbal speech sounds in human communication. In: Esposito, A., Faundez-Zanuy, M., Keller, E., Marinaro, M. (eds.) Verbal and Nonverbal Communication Behaviours. LNCS, vol. 4775, pp. 117–128. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-76442-7_11 CrossRefGoogle Scholar
  5. 5.
    Campbell, W.N.: Prosodic encoding of English speech. In: Second International Conference on Spoken Language Processing (1992)Google Scholar
  6. 6.
    Cohn, A.C., Fougeron, C., Huffman, M.K.: The Oxford Handbook of Laboratory Phonology. Oxford University Press, Oxford (2012). Sect. 6.2, pp. 103–114Google Scholar
  7. 7.
    Cole, J., Mo, Y., Hasegawa-Johnson, M.: Signal-based and expectation-based factors in the perception of prosodic prominence. Lab. Phonol. 1(2), 425–452 (2010)CrossRefGoogle Scholar
  8. 8.
    Galliano, S., Geoffrois, E., Mostefa, D., Choukri, K., Bonastre, J.F., Gravier, G.: The ESTER phase II evaluation campaign for the rich transcription of French broadcast news. In: INTERSPEECH, pp. 1149–1152 (2005)Google Scholar
  9. 9.
    Heldner, M.: On the reliability of overall intensity and spectral emphasis as acoustic correlates of focal accents in swedish. J. Phon. 31(1), 39–62 (2003)CrossRefGoogle Scholar
  10. 10.
    Honnet, P.E., Lazaridis, A., Garner, P.N., Yamagishi, J.: The SIWIS French speech synthesis database? Design and recording of a high quality French database for speech synthesis. Technical report, Idiap (2017)Google Scholar
  11. 11.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  12. 12.
    Li, K., Meng, H.: Automatic lexical stress and pitch accent detection for L2 English speech using multi-distribution deep neural networks. Speech Commun. (2016)Google Scholar
  13. 13.
    Li, K., Zhang, S., Li, M., Lo, W.K., Meng, H.M.: Prominence model for prosodic features in automatic lexical stress and pitch accent detection. In: INTERSPEECH, pp. 2009–2012 (2011)Google Scholar
  14. 14.
    Narupiyakul, L., Keselj, V., Cercone, N., Sirinaovakul, B.: Focus to emphasize tone analysis for prosodic generation. Comput. Math. Appl. 55(8), 1735–1753 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Noth, E., Batliner, A., Kießling, A., Kompe, R., Niemann, H.: Verbmobil: the use of prosody in the linguistic components of a speech understanding system. IEEE Trans. Speech Audio Process. 8(5), 519–532 (2000)CrossRefGoogle Scholar
  16. 16.
    Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., et al.: The kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding, No. EPFL-CONF-192584. IEEE Signal Processing Society (2011)Google Scholar
  17. 17.
    Shriberg, E., Stolcke, A., Hakkani-Tür, D., Tür, G.: Prosody-based automatic segmentation of speech into sentences and topics. Speech Commun. 32(1), 127–154 (2000)CrossRefGoogle Scholar
  18. 18.
    Sluijter, A.M., Shattuck-Hufnagel, S., Stevens, K.N., Van Heuven, V., et al.: Supralaryngeal resonance and glottal pulse shape as correlates of prosodic stress and accent in American English (1995)Google Scholar
  19. 19.
    Sluijter, A.M., Van Heuven, V.J.: Spectral balance as an acoustic correlate of linguistic stress. J. Acoust. Soc. Am. 100(4), 2471–2485 (1996)CrossRefGoogle Scholar
  20. 20.
    Streefkerk, B.M., Pols, L.C., Ten Bosch, L., et al.: Automatic detection of prominence (as defined by listeners’ judgements) in read aloud Dutch sentences. In: ICSLP (1998)Google Scholar
  21. 21.
    Tepperman, J., Narayanan, S.: Automatic syllable stress detection using prosodic features for pronunciation evaluation of language learners. In: IEEE International Conference on Proceedings of the Acoustics, Speech, and Signal Processing (ICASSP 2005), vol. 1, pp. I–937. IEEE (2005)Google Scholar
  22. 22.
    Van Kuijk, D., Boves, L.: Acoustic characteristics of lexical stress in continuous telephone speech. Speech Commun. 27(2), 95–111 (1999)CrossRefGoogle Scholar
  23. 23.
    Wheatley, B., Doddington, G., Hemphill, C., Godfrey, J., Holliman, E., McDaniel, J., Fisher, D.: Robust automatic time alignment of orthographic transcriptions with unconstrained speech. In: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-1992, vol. 1, pp. 533–536. IEEE (1992)Google Scholar
  24. 24.
    Wightman, C.W., Ostendorf, M.: Automatic labeling of prosodic patterns. IEEE Trans. Speech Audio Process. 2(4), 469–481 (1994)CrossRefGoogle Scholar
  25. 25.
    Yu, K., Mairesse, F., Young, S.: Word-level emphasis modelling in HMM-based speech synthesis. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 4238–4241. IEEE (2010)Google Scholar
  26. 26.
    Zeiler, M.D., Ranzato, M., Monga, R., Mao, M., Yang, K., Le, Q.V., Nguyen, P., Senior, A., Vanhoucke, V., Dean, J., et al.: On rectified linear units for speech processing. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3517–3521. IEEE (2013)Google Scholar
  27. 27.
    Zhao, J., Yuan, H., Liu, J., Xia, S.: Automatic lexical stress detection using acoustic features for computer assisted language learning. In: Proceedings of the APSIPA ASC, pp. 247–251 (2011)Google Scholar
  28. 28.
    Zhu, Y., Liu, J., Liu, R.: Automatic lexical stress detection for English learning. In: Proceedings of the 2003 International Conference on Natural Language Processing and Knowledge Engineering, pp. 728–733. IEEE (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Abdelwahab Heba
    • 1
    • 2
  • Thomas Pellegrini
    • 2
  • Tom Jorquera
    • 1
  • Régine André-Obrecht
    • 2
  • Jean-Pierre Lorré
    • 1
  1. 1.LinagoraToulouseFrance
  2. 2.IRITUniversité de ToulouseToulouseFrance

Personalised recommendations