Abstract
In this paper, we focus on automatic recognition of telephone conversational speech in scenario, when no amount of genuine telephone recordings is available for training. The training set contains only data from a significantly different domain, such as recording of broadcast news. Significant mismatch arises between training and test conditions, which leads to deteriorated performance of the resulting recognition system. We aim to diminish this mismatch using the data augmentation.
Speech compression and narrow-band spectrum are significant features of the telephone speech. We apply these effects to the training dataset artificially, in order to make it more similar to the desired test conditions. Using such augmented dataset, we subsequently train an acoustic model. Our experiments show that the augmented models achieve accuracy close to the results of a model trained on genuine telephone data. Moreover, when the augmentation is applied to the real-world telephone data, further accuracy gains are achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amodei, D., et al.: Deep speech 2: End-to-end speech recognition in English and Mandarin. In: International Conference on Machine Learning, pp. 173–182 (2016)
Borsky, M., Mizera, P., Pollak, P., Nouza, J.: Dithering techniques in automatic recognition of speech corrupted by MP3 compression: analysis, solutions and experiments. Speech Commun. 86, 75–84 (2017)
Borsky, M., Pollak, P., Mizera, P.: Advanced acoustic modelling techniques in MP3 speech recognition. EURASIP J. Audio Speech Music Process. 2015(1), 20 (2015)
Cui, X., Goel, V., Kingsbury, B.: Data augmentation for deep neural network acoustic modeling. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 23(9), 1469–1477 (2015)
Dahl, G., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20(1), 30–42 (2012). https://doi.org/10.1109/TASL.2011.2134090
FFmpeg team: Ffmpeg - cross-platform solution to record, convert and stream audio and video. Software version: 20170525–b946bd8. https://www.ffmpeg.org/
Fraga-Silva, T., et al.: Active learning based data selection for limited resource STT and KWS. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)
Fraga-Silva, T., et al.: Improving data selection for low-resource STT and KWS. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 153–159. IEEE (2015)
Garofolo, J.S., et al.: TIMIT acoustic-phonetic continuous speech corpus. Linguist. Data Consortium, 10(5) (1993)
Jaitly, N., Hinton, G.E.: Vocal tract length perturbation (VTLP) improves speech recognition. In: Proceeding of the ICML Workshop on Deep Learning for Audio, Speech and Language, pp. 625–660 (2013)
Kanda, N., Takeda, R., Obuchi, Y.: Elastic spectral distortion for low resource speech recognition with deep neural networks. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 309–314. IEEE (2013)
Kemp, T., Waibel, A.: Unsupervised training of a speech recognizer: recent experiments. In: Eurospeech (1999)
Kinoshita, K., et al.: A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research. EURASIP J. Adv. Signal Process. 2016(1), 7 (2016)
Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: 1995 International Conference on Acoustics, Speech, and Signal Processing 1995, ICASSP 1995, vol. 1, pp. 181–184. IEEE (1995)
Ko, T., Peddinti, V., Povey, D., Khudanpur, S.: Audio augmentation for speech recognition. In: INTERSPEECH, pp. 3586–3589 (2015)
Li, J., Deng, L., Gong, Y., Haeb-Umbach, R.: An overview of noise-robust automatic speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 22(4), 745–777 (2014)
Ma, J., Schwartz, R.: Unsupervised versus supervised training of acoustic models. In: Ninth Annual Conference of the International Speech Communication Association (2008)
Mammone, R.J., Zhang, X., Ramachandran, R.P.: Robust speaker recognition: a feature-based approach. IEEE Signal Process. Mag. 13(5), 58 (1996)
Polacky, J., Jarina, R., Chmulik, M.: Assessment of automatic speaker verification on lossy transcoded speech. In: 2016 4th International Workshop on Biometrics and Forensics (IWBF), pp. 1–6. IEEE (2016)
Raghavan, S., et al.: A comparative study on the effect of different codecs on speech recognition accuracy using various acoustic modeling techniques. In: 2017 Twenty-third National Conference on Communications (NCC), pp. 1–6. IEEE (2017)
Seltzer, M.L., Yu, D., Wang, Y.: An investigation of deep neural networks for noise robust speech recognition. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7398–7402. IEEE (2013)
Siegert, I., Lotz, A.F., Maruschke, M., Jokisch, O., Wendemuth, A.: Emotion intelligibility within codec-compressed and reduced bandwidth speech. In: ITG Symposium, Proceedings of Speech Communication, vol. 12, pp. 1–5. VDE (2016)
Torch team: Torch - a scientific computing framework for luajit. http://torch.ch
Vincent, E., Watanabe, S., Nugraha, A.A., Barker, J., Marxer, R.: An analysis of environment, microphone and data simulation mismatches in robust speech recognition. Comput. Speech Lang. 46, 535–557 (2016)
Xiong, W., et al.: The Microsoft 2016 conversational speech recognition system. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5255–5259. IEEE (2017)
Young, S., Young, S.: The HTK hidden Markov model toolkit: design and philosophy. Entrop. Cambridge Res. Lab. Ltd. 2, 2–44 (1994)
Acknowledgments
This work was supported by the Technology Agency of the Czech Republic (Project No. TH03010018).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Málek, J., Ždánský, J., Červa, P. (2018). Robust Recognition of Conversational Telephone Speech via Multi-condition Training and Data Augmentation. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-00794-2_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00793-5
Online ISBN: 978-3-030-00794-2
eBook Packages: Computer ScienceComputer Science (R0)