Skip to main content
SpringerLink
Log in

Spectral Tilt Estimation for Speech Intelligibility Enhancement Using RNN Based on All-Pole Model

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11296))

Included in the following conference series:

Abstract

Speech intelligibility enhancement is extremely meaningful for successful speech communication in noisy environments. Several methods based on Lombard effect are used to increase intelligibility. In those methods, spectral tilt has been suggested to be a significant characteristic to produce Lombard speech that is more intelligible than normal speech. All-pole model computed by some methods has been used to capture the accurate spectral tilt of high-quality speech, but they are not appropriate for the spectral tilt estimation of telephone speech. In this paper, recurrent neural networks (RNNs) are used to estimate the tilt of telephone speech in German and English. RNN-based spectral tilt estimation show the robustness on the change of the all-pole model order and phonation type for narrow and wideband speech. Mean squared error (MSE) of spectral tilt estimation using RNN-based method is increased by about 26.20% in narrow speech and 19.49% in wideband speech comparing to the DNN-based measure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kleijn, W.B., Crespo, J.B., Hendriks, R.C., et al.: Optimizing speech intelligibility in a noisy environment: a unified view. IEEE Signal Process. Mag. 32(2), 43–54 (2015)

    Article  Google Scholar 

  2. Sauert, B., Vary, P.: Near end listening enhancement optimized with respect to speech intelligibility index. In: 17th European Signal Processing Conference, pp. 1844–1848. IEEE (2009)

    Google Scholar 

  3. Schepker, H., Rennies, J., Doclo, S.: Improving speech intelligibility in noise by SII-dependent preprocessing using frequency-dependent amplification and dynamic range compression. In: Proceedings of Interspeech, pp. 3577–3581. ISCA, Lyon (2013)

    Google Scholar 

  4. Petkov, P.N., Kleijn, W.B.: Spectral dynamics recovery for enhanced speech intelligibility in noise. IEEE/ACM Trans. Audio Speech Lang. Process. 23(2), 327–338 (2015)

    Article  Google Scholar 

  5. Petko, P.N., Stylinaou, Y.: Adaptive gain control and time warp for enhanced speech intelligibility under reverberation. In: IEEE International Conference on Acoustic, Speech and Signal Processing (IASSP), New Orleans, pp. 691–695. IEEE (2017)

    Google Scholar 

  6. Zorilâ, T.C., Kandia, V., Stylianou, Y.: Speech-in-noise intelligibility improvement based on spectral shaping and dynamic range compression. In: Proceedings Interspeech, pp. 635–638. ISCA, Portland (2012)

    Google Scholar 

  7. Zorilâ, T.C., Stylianou, Y., Ishihara, T., et al.: Near and far field speech-in-noise intelligibility improvements based on a time-frequency energy reallocation approach. IEEE Trans. Audio Speech Lang. Process. 24(10), 1808–1818 (2016)

    Article  Google Scholar 

  8. Jokinen, E., Remes, U., Takanen, M., et al: Spectral tilt modelling with GMMs for intelligibility enhancement of narrowband telephone speech. In: Proceedings of Interspeech, pp. 2036–2040. ISCA, Singapore (2014)

    Google Scholar 

  9. Jokinen, E., Remes, U., Alku, P.: The use of read versus conversational Lombard speech in spectral tilt modeling for intelligibility enhancement in near-end noise conditions. In: Proceedings of Interspeech, pp. 2771–2775. ISCA, San Francisco (2016)

    Google Scholar 

  10. Jokinen, E., Remes, U., Alku, P.: Intelligibility enhancement of telephone speech using gaussian process regression for normal-to-lombard spectral tilt conversion. IEEE Trans. Audio Speech Lang. Process. 25(10), 1985–1996 (2017)

    Article  Google Scholar 

  11. Summers, W.V., Pisoni, D.B., Bernacki, R.H., et al.: Effects of noise on speech production: acoustic and perceptual analyses. J. Acoust. Soc. Am. 3(84), 917–928 (1988)

    Article  Google Scholar 

  12. Bronkhorst, A.W.: The cocktail party phenomenon: a review of research on speech intelligibility in multiple-talker conditions. Acta Acust. United Acust. 86(1), 117–128 (2000)

    Google Scholar 

  13. Lu, Y., Cooke, M.: The contribution of change in F0 and spectral tilt to increased intelligibility of speech produced in noise. Speech Commun. 51(12), 1253–1262 (2009)

    Article  Google Scholar 

  14. Cooke, M., Lu, Y.: Spectral and temporal changes to speech produced in the presence of energetic and informational masker. J. Acoust. Soc. Am. 128(4), 2059–2069 (2010)

    Article  Google Scholar 

  15. Jokinen, E., Alku, P.: Estimating the spectral tilt of the glottal source from telephone speech using neural network. J. Acoust. Soc. Am. Express Lett. 141(4), 327–330 (2017)

    Article  Google Scholar 

  16. Makhoul, J.: Linear prediction: a tutorial review. Proc. IEEE 63(4), 561–580 (1975)

    Article  Google Scholar 

  17. El-Jaroudi, A., Makhoul, J.: Discrete all-pole modeling. IEEE Trans. Signal Process. 39(2), 411–423 (1991)

    Article  Google Scholar 

  18. Ma, C., Kamp, Y., Willems, L.F.: Robust signal selection for linear prediction analysis of voiced speech. Speech Commun. 12(1), 69–81 (1993)

    Article  Google Scholar 

  19. Magi, C., Pohjalainen, J.: Stabilised weighted linear prediction. Speech Commun. 51(5), 401–411 (2009)

    Article  Google Scholar 

  20. Airaksinen, M., Story, B., Alku, P.: Quasi closed phase analysis for glottal inverse filtering. In: 14th Annual Conference of the International Speech Communication Association, pp. 143–147. ISCA, Lyon (2013)

    Google Scholar 

  21. Airaksinen, M., Raitio, T., Story, B., et al.: Quasi closed phase glottal inverse filtering analysis with weighted linear prediction. IEEE Trans. Audio Speech Lang. Process. 22(3), 596–607 (2014)

    Article  Google Scholar 

  22. Drugman, T., Thomas, M., Gudnason, J., et al.: Detection of glottal closure instants from speech signal: a quantitative review. IEEE Trans. Audio Speech Lang. Process. 20(3), 994–1006 (2012)

    Article  Google Scholar 

  23. Sofoklis, K., Okko, R., Pavvo, A.: Evaluation of spectral tilt measures for sentence prominence under different noise conditions. In: Proceedings of Interspeech, pp. 3211–3215. ISCA, Stockholm (2017)

    Google Scholar 

  24. Lopez, A.R., Seshadri, S., Juvela, L., et al.: Speaking style conversion from normal to Lombard speech using a glottal vocoder and Bayesian GMMs. In: Proceedings of Interspeech, pp. 1363–1367. ISCA, Stockholm (2017)

    Google Scholar 

  25. Tsoi, A.C., Back, A.: Discrete time recurrent neural network architectures: a unifying review. Neurocomputing 15(3–4), 183–223 (1997)

    Article  Google Scholar 

  26. Sołoducha, M., Raake, A., Kettler, F., Voigt, P.: Lombard speech database for German language. In: Proceedings of DAGA, Aachen (2016)

    Google Scholar 

  27. Cooke, M., Mayo, C., Valentini-Botinhao, C., et al.: Evaluating the intelligibility benefit of speech modifications in known noise conditions. Speech Commun. 55(4), 572–585 (2013)

    Article  Google Scholar 

  28. Rothauser, E.H.: IEEE recommended practice for speech quality measurements. IEEE Trans. Audio Electroacoust. 17, 225–246 (1969)

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by National Key Program of China (No. 2017YFB1002803) and National Nature Science Foundation of China (No. U1736206, No. 61801334, No. 61762005).

Author information

Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China

    Rui Zhang, Ruimin Hu, Gang Li & Xiaochen Wang

  2. Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China

    Rui Zhang, Ruimin Hu & Gang Li

  3. Collaborative Innovation Center of Geospatial Technology, Wuhan, China

    Xiaochen Wang

Authors
  1. Rui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruimin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaochen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruimin Hu .

Editor information

Editors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. EURECOM, Sophia Antipolis, France

    Benoit Huet

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Vasileios Mezaris

  4. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  5. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  6. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, R., Hu, R., Li, G., Wang, X. (2019). Spectral Tilt Estimation for Speech Intelligibility Enhancement Using RNN Based on All-Pole Model. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05716-9_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05715-2

  • Online ISBN: 978-3-030-05716-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation