Abstract
This paper demonstrates how standard feature extraction methods such as PLP can be successfully replaced by a neural network and methods such as mean normalization, variance normalization and delta coefficients can be simultaneously utilized in a neural-network-based acoustic model. Our experiments show that this replacement is significantly beneficial. Moreover, in our experiments, also a neural-network-based voice activity detector was employed and trained simultaneously with a neural-network-based feature extraction and a neural-network-based acoustic model. The system performance was evaluated on the British English speech corpus WSJCAM0.
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References
Grézl, F., Karafiát, M.: Semi-supervised bootstrapping approach for neural network feature extractor training. In: ASRU, pp. 470–475. IEEE (2013)
Sainath, T.N., Kingsbury, B., Mohamed, A.R, Ramabhadran, B.: Learning filter banks within a deep neural network framework. In: ASRU, pp. 297–302. IEEE (2013)
Narayanan, A., Wang, D.: Ideal ratio mask estimation using deep neural networks for robust speech recognition. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7092–7096, May 2013
Fernandez Astudillo, R., Abad, A., Trancoso, I.: Accounting for the residual uncertainty of multi-layer perceptron based features. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6859–6863, May 2014
Seps, L., Málek, J., Cerva, P., Nouza, J.: Investigation of deep neural networks for robust recognition of nonlinearly distorted speech. In: INTERSPEECH, pp. 363–367 (2014)
Sainath, T.N., Peddinti, V., Kingsbury, B., Fousek, P., Ramabhadran, B., Nahamoo, D.: Deep scattering spectra with deep neural networks for LVCSR tasks. In: 15th Annual Conference of the International Speech Communication Association, INTERSPEECH 2014, September 14–18, 2014, Singapore, pp. 900–904 (2014)
Tüske, Z., Golik, P., Schlüter, R., Ney, H.: Acoustic modeling with deep neural networks using raw time signal for lvcsr. In: Interspeech, Singapore, pp. 890–894, September 2014
Heřmanský, H.: Perceptual linear predictive (PLP) analysis of speech. J. Acoust. Soc. Am. 57(4), 1738–52 (1990)
Chang, S., Morgan, N.: Robust CNN-based speech recognition with Gabor filter kernels. In: 15th Annual Conference of the International Speech Communication Association, INTERSPEECH 2014, September 14–18, 2014, Singapore, pp. 905–909 (2014)
Robinson, T., Fransen, J., Pye, D., Foote, J., Renals, S.: Wsjcam0: a british english speech corpus for large vocabulary continuous speech recognition. In: Proc. ICASSP 1995, pp. 81–84. IEEE (1995)
Psutka, J., Švec, J., Psutka, J.V., Vaněk, J., Pražák, A., Šmídl, L.: Fast phonetic/lexical searching in the archives of the czech holocaust testimonies: advancing towards the MALACH project visions. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2010. LNCS, vol. 6231, pp. 385–391. Springer, Heidelberg (2010)
Pražák, A., Psutka, J.V., Psutka, Loose, Z.: Towards live subtitling of TV ice-hockey commentary. In: Cabello, E., Virvou, M., Obaidat, M.S., Ji, H., Nicopolitidis, P., Vergados, D.D. (eds.) SIGMAP, pp. 151–155. SciTePress (2013)
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Zelinka, J., Vaněk, J., Müller, L. (2015). Simultaneously Trained NN-Based Acoustic Model and NN-Based Feature Extractor. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_27
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DOI: https://doi.org/10.1007/978-3-319-24033-6_27
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