Advertisement

Towards Improving Intelligibility of Black-Box Speech Synthesizers in Noise

  • Thomas ManziniEmail author
  • Alan BlackEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11096)

Abstract

This paper explores how different synthetic speech systems can be understood in a noisy environment that resembles radio noise. This work is motivated by a need for intelligible speech in noisy environments such as emergency response and disaster notification. We discuss prior work done on listening tasks as well as speech in noise. We analyze three different speech synthesizers in three different noise settings. We measure quantitatively the intelligibility of each synthesizer in each noise setting based on human performance on a listening task. Finally, treating the synthesizer and its generated audio as a black box, we present how word level and sentence level input choices can lead to increased or decreased listener error rates for synthesized speech.

Keywords

Speech Synthesized Noise Radio Intelligibility 

Notes

Acknowledgments

We would like to acknowledge several people for their help and support on this work. Particularly Carolyn Penstein, Rajat Kulshreshtha, Abhilasha Ravichander, and the officers of CMU EMS. As well as the several people who helped edit this work, especially Elise Romberger. Finally, thank you to reviewers reading and examining our experiments, methodology, and submission.

References

  1. 1.
    Black, A.W., Lenzo, K.A.: Flite: a small fast run-time synthesis engine. In: 4th ISCA Tutorial and Research Workshop (ITRW) on Speech Synthesis (2001)Google Scholar
  2. 2.
    Cooke, M.: A glimpsing model of speech perception in noise. J. Acoust. Soc. Am. 119(3), 1562–1573 (2006)CrossRefGoogle Scholar
  3. 3.
    Dau, T., Püschel, D., Kohlrausch, A.: A quantitative model of the “effective” signal processing in the auditory system. i. model structure. J. Acoust. Soc. Am. 99(6), 3615–3622 (1996)CrossRefGoogle Scholar
  4. 4.
    Davies, M.: The corpus of contemporary American English (Coca): 450 million words, 1990–2012. Brigham Young University (2002)Google Scholar
  5. 5.
    Duddington, J.: eSpeak text to speech (2012)Google Scholar
  6. 6.
    Durette, P.N.: gTTS: a python interface for google’s text to speech api (2017). https://github.com/pndurette/gTTS. Accessed 15 Apr 2018
  7. 7.
    Fiedrich, F., Burghardt, P.: Agent-based systems for disaster management. Commun. ACM 50(3), 41–42 (2007)CrossRefGoogle Scholar
  8. 8.
    Imran, M., Castillo, C., Lucas, J., Meier, P., Vieweg, S.: AIDR: Artificial intelligence for disaster response. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 159–162. ACM (2014)Google Scholar
  9. 9.
    Kamath, S., Loizou, P.: A multi-band spectral subtraction method for enhancing speech corrupted by colored noise. In: ICASSP, vol. 4, pp. 44164–44164. Citeseer (2002)Google Scholar
  10. 10.
    Killion, M.C., Niquette, P.A., Gudmundsen, G.I., Revit, L.J., Banerjee, S.: Development of a quick speech-in-noise test for measuring signal-to-noise ratio loss in normal-hearing and hearing-impaired listeners. J. Acoust. Soc. Am. 116(4), 2395–2405 (2004)CrossRefGoogle Scholar
  11. 11.
    McAulay, R., Malpass, M.: Speech enhancement using a soft-decision noise suppression filter. IEEE Trans. Acoust. Speech Signal Process. 28(2), 137–145 (1980)CrossRefGoogle Scholar
  12. 12.
    Park, Y., Patwardhan, S., Visweswariah, K., Gates, S.C.: An empirical analysis of word error rate and keyword error rate. In: Ninth Annual Conference of the International Speech Communication Association (2008)Google Scholar
  13. 13.
    Pichora-Fuller, M.K., Schneider, B.A., Daneman, M.: How young and old adults listen to and remember speech in noise. J. Acoust. Soc. Am. 97(1), 593–608 (1995)CrossRefGoogle Scholar
  14. 14.
    Ravichander, A., Manzini, T., Grabmair, M., Neubig, G., Francis, J., Nyberg, E.: How would you say it? eliciting lexically diverse dialogue for supervised semantic parsing. In: Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pp. 374–383 (2017)Google Scholar
  15. 15.
    Schmidt-Nielsen, A.: Intelligibility and acceptability testing for speech technology. Technical report, Naval Research Lab, Washington DC (1992)Google Scholar
  16. 16.
    Valentini-Botinhao, C., Yamagishi, J., King, S.: Can objective measures predict the intelligibility of modified hmm-based synthetic speech in noise? In: Twelfth Annual Conference of the International Speech Communication Association (2011)Google Scholar
  17. 17.
    Valentini-Botinhao, C., Yamagishi, J., King, S.: Evaluation of objective measures for intelligibility prediction of hmm-based synthetic speech in noise. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5112–5115. IEEE (2011)Google Scholar
  18. 18.
    Varga, A., Steeneken, H.J.: Assessment for automatic speech recognition: Ii. noisex-92: a database and an experiment to study the effect of additive noise on speech recognition systems. Speech Commun. 12(3), 247–251 (1993)CrossRefGoogle Scholar
  19. 19.
    Wang, Y.Y., Acero, A., Chelba, C.: Is word error rate a good indicator for spoken language understanding accuracy. In: 2003 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2003, pp. 577–582. IEEE (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations