Abstract
This paper considers an assessment and evaluation of the pronunciation quality in computer-aided language learning systems. We propose the novel distortion measure for speech processing by using the gain optimization of the symmetrized Itakura-Saito divergence. This dissimilarity is implemented in a complete algorithm for pronunciation learning and improvement. At its first stage, a user has to achieve a stable pronunciation of all sounds by matching them with sounds of an ideal speaker. At the second stage, the recognition of sounds and their short sequences is carried out to guarantee the distinguishability of learned sounds. The training set may contain not only ideal sounds but the best utterances of a user obtained at the previous step. Finally, the word recognition accuracy is estimated by using deep neural networks fine-tuned on the best words from a user. Experimental study shows that the proposed procedure makes it possible to achieve high efficiency for learning of sounds and their sequences even in the presence of noise in an observed utterance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Golonka, E.M., Bowles, A.R., Frank, V.M., Richardson, D.L., Freynik, S.: Technologies for foreign language learning: a review of technology types and their effectiveness. Comput. Assist. Lang. Learn. 27(1), 70–105 (2014)
Sztahó, D., Kiss, G., Vicsi, K.: Computer based speech prosody teaching system. Comput. Speech Lang. 50, 126–140 (2018)
Han, K.I., Park, H.J., Lee, K.M.: Speech recognition and lip shape feature extraction for English vowel pronunciation of the hearing-impaired based on SVM technique. In: Proceedings of the International Conference on Big Data and Smart Computing (BigComp), pp. 293–296. IEEE (2016)
Hu, W., Qian, Y., Soong, F.K.: A new DNN-based high quality pronunciation evaluation for computer-aided language learning (CALL). In: Proceedings of Interspeech, pp. 1886–1890 (2013)
Kneller, E., Karaulnyh, D.: System and method of converting voice signal into transcript presentation with metadata. RU Patent 2589851 C2, 10 July 2016
Agarwal, C., Chakraborty, P.: A review of tools and techniques for computer aided pronunciation training (CAPT) in English. Educ. Inf. Technol. 24(6), 3731–3743 (2019). https://doi.org/10.1007/s10639-019-09955-7
Haikun, T., Shiying, W., Xinsheng, L., Yue, X.G.: Speech recognition model based on deep learning and application in pronunciation quality evaluation system. In: Proceedings of the International Conference on Data Mining and Machine Learning, pp. 1–5 (2019)
Savchenko, V.V.: Minimum of information divergence criterion for signals with tuning to speaker voice in automatic speech recognition. Radioelectron. Commun. Syst. 63(1), 42–54 (2020). https://doi.org/10.3103/S0735272720010045
Franco, H., Bratt, H., Rossier, R., Rao Gadde, V., Shriberg, E., Abrash, V., Precoda, K.: Eduspeak®: a speech recognition and pronunciation scoring toolkit for computer-aided language learning applications. Lang. Test. 27(3), 401–418 (2010)
Sudhakara, S., Ramanathi, M.K., Yarra, C., Ghosh, P.K.: An improved goodness of pronunciation (GoP) measure for pronunciation evaluation with DNN-HMM system considering hmm transition probabilities. In: Proceedings of Interspeech, pp. 954–958 (2019)
Arias, J.P., Yoma, N.B., Vivanco, H.: Automatic intonation assessment for computer aided language learning. Speech Commun. 52(3), 254–267 (2010)
Elaraby, M.S., Abdallah, M., Abdou, S., Rashwan, M.: A deep neural networks (DNN) based models for a computer aided pronunciation learning system. In: Ronzhin, A., Potapova, R., Németh, G. (eds.) SPECOM 2016. LNCS (LNAI), vol. 9811, pp. 51–58. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43958-7_5
Huang, G., Ye, J., Shen, Y., Zhou, Y.: A evaluating model of English pronunciation for Chinese students. In: Proceedings of the 9th International Conference on Communication Software and Networks (ICCSN), pp. 1062–1065. IEEE (2017)
Xiao, Y., Soong, F., Hu, W.: Paired phone-posteriors approach to ESL pronunciation quality assessment. In: Proceedings of Interspeech, pp. 1631–1635 (2018)
Srinivasan, A., Yarra, C., Ghosh, P.K.: Automatic assessment of pronunciation and its dependent factors by exploring their interdependencies using DNN and LSTM. In: Proceedings of the 8th ISCA Workshop on Speech and Language Technology in Education (SLaTE), pp. 30–34 (2019)
Gu, L., Harris, J.G.: SLAP: a system for the detection and correction of pronunciation for second language acquisition. In: Proceedings of the International Symposium on Circuits and Systems (ISCAS), vol. 2, p. II. IEEE (2003)
Gray, R., Buzo, A., Gray, A., Matsuyama, Y.: Distortion measures for speech processing. IEEE Trans. Acoust. Speech Signal Process. 28(4), 367–376 (1980)
Benesty, J., Sondhi, M.M., Huang, Y.A. (eds.): Springer Handbook of Speech Processing. SH. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-49127-9
Mošner, L., et al.: Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6475–6479. IEEE (2019)
Savchenko, A.V., Savchenko, L.V.: Towards the creation of reliable voice control system based on a fuzzy approach. Pattern Recogn. Lett. 65, 145–151 (2015)
Savchenko, L.V., Savchenko, A.V.: Fuzzy phonetic decoding method in a phoneme recognition problem. In: Drugman, T., Dutoit, T. (eds.) NOLISP 2013. LNCS (LNAI), vol. 7911, pp. 176–183. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38847-7_23
Su, H.Y., Gao, Y.: Adaptive gain reduction for encoding a speech signal. US Patent 9,269,365, 23 February 2016
Dionelis, N., Brookes, M.: Speech enhancement using modulation-domain Kalman filtering with active speech level normalized log-spectrum global priors. In: Proceedings of the 25th European Signal Processing Conference (EUSIPCO), pp. 2309–2313. IEEE (2017)
Erkelens, J., Jensen, J., Heusdens, R.: A data-driven approach to optimizing spectral speech enhancement methods for various error criteria. Speech Commun. 49(7–8), 530–541 (2007)
Bastos, I., Oliveira, L.B., Goes, J., Silva, M.: MOSFET-only wideband LNA with noise cancelling and gain optimization. In: Proceedings of the 17th International Conference Mixed Design of Integrated Circuits and Systems (MIXDES), pp. 306–311. IEEE (2010)
Itakura, F., Saito, S.: Analysis synthesis telephony based on the maximum likelihood method. In: Proceedings of the 6th International Congress on Acoustics, pp. 17–20 (1968)
Marple Jr., S.L.: Digital Spectral Analysis with Applications, 2nd edn. Dover Publications, Mineola, New York (2019). 432 p.
Savchenko, V.V.: Itakura–Saito divergence as an element of the information theory of speech perception. J. Commun. Technol. Electron. 64(6), 590–596 (2019). https://doi.org/10.1134/S1064226919060093
Kullback, S.: Information Theory and Statistics. Dover Publications, New York (1997)
Savchenko, A.V., Belova, N.S.: Statistical testing of segment homogeneity in classification of piecewise-regular objects. Int. J. Appl. Math. Comput. Sci. 25(4), 915–925 (2015)
Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23(1), 67–72 (1975)
Savchenko, V.V., Savchenko, L.V.: Method for measuring the intelligibility of speech signals in the Kullback–Leibler information metric. Meas. Tech. 62(9), 832–839 (2019). https://doi.org/10.1007/s11018-019-01702-1
Sainath, T.N., Parada, C.: Convolutional neural networks for small-footprint keyword spotting. In: Proceedings of the Sixteenth Annual Conference of the International Speech Communication Association, pp. 1478–1482 (2015)
Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Bengio, C.L.Y., Courville, A.: Towards end-to-end speech recognition with deep convolutional neural networks. arXiv preprint arXiv:1701.02720 (2017)
Nakkiran, P., Alvarez, R., Prabhavalkar, R., Parada, C.: Compressing deep neural networks using a rank-constrained topology. In: Proceedings of the Sixteenth Annual Conference of the International Speech Communication Association, pp. 1473–1477 (2015)
Kuchaiev, O., et al.: Nemo: a toolkit for building AI applications using neural modules. arXiv preprint arXiv:1909.09577 (2019)
Acknowledgements
The work was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Savchenko, A.V., Savchenko, V.V., Savchenko, L.V. (2020). Optimization of Gain in Symmetrized Itakura-Saito Discrimination for Pronunciation Learning. In: Kononov, A., Khachay, M., Kalyagin, V., Pardalos, P. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2020. Lecture Notes in Computer Science(), vol 12095. Springer, Cham. https://doi.org/10.1007/978-3-030-49988-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-49988-4_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49987-7
Online ISBN: 978-3-030-49988-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)