Abstract
This paper presents a conversational speech recognition system able to operate in non-stationary reverberated environments. The system is composed of a dereverberation front-end exploiting multiple distant microphones, and a speech recognition engine. The dereverberation front-end identifies a room impulse response by means of a blind channel identification stage based on the Unconstrained Normalized Multi-Channel Frequency Domain Least Mean Square algorithm. The dereverberation stage is based on the adaptive inverse filter theory and uses the identified responses to obtain a set of inverse filters which are then exploited to estimate the clean speech. The speech recognizer is based on tied-state cross-word triphone models and decodes features computed from the dereverberated speech signal. Experiments conducted on the Buckeye corpus of conversational speech report a relative word accuracy improvement of 17.48% in the stationary case and of 11.16% in the non-stationary one.
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References
Haque, M., Hasan, M.: Noise robust multichannel frequency-domain LMS algorithms for blind channel identification. IEEE Signal Process. Lett. 15, 305–308 (2008)
Hikichi, T., Delcroix, M., Miyoshi, M.: Inverse filtering for speech dereverberation less sensitive to noise and room transfer function fluctuations. EURASIP Journal on Advances in Signal Process. 2007(1) (2007)
Huang, Y., Benesty, J.: A class of frequency domain adaptive approaches to blind multichannel identification. IEEE Trans. Speech Audio Process. 51(1), 11–24 (2003)
Kumar, K., Singh, R., Raj, B., Stern, R.: Gammatone sub-band magnitude-domain dereverberation for ASR. In: Proc. of ICASSP, pp. 4604–4607 (May 2011)
Miyoshi, M., Kaneda, Y.: Inverse filtering of room acoustics. IEEE Trans. Signal Process. 36(2), 145–152 (1988)
Naylor, P., Gaubitch, N.: Speech Dereverberation. Signals and Communication Technology. Springer (2010)
Pitt, M., Dilley, L., Johnson, K., Kiesling, S., Raymond, W., Hume, E., Fosler-Lussier, E.: Buckeye corpus of conversational speech, 2nd release (2007), http://www.buckeyecorpus.osu.edu , Columbus, OH: Department of Psychology, Ohio State University (Distributor)
Principi, E., Cifani, S., Rocchi, C., Squartini, S., Piazza, F.: Keyword spotting based system for conversation fostering in tabletop scenarios: Preliminary evaluation. In: Proc. of 2nd Int. Conf. on Human System Interaction, Catania, pp. 216–219 (2009)
Principi, E., Cifani, S., Rotili, R., Squartini, S., Piazza, F.: Comparative evaluation of single-channel MMSE-based noise reduction schemes for speech recognition. Journal of Electrical and Computer Engineering 2010, 6 (2010)
Rotili, R., Cifani, S., Principi, E., Squartini, S., Piazza, F.: A robust iterative inverse filtering approach for speech dereverberation in presence of disturbances. In: Proc. of IEEE APCCAS, pp. 434–437 (December 2008)
Schuller, B., Batliner, A., Steidl, S., Seppi, D.: Recognising realistic emotions and affect in speech: state of the art and lessons learnt from the first challenge. Speech Communication, 1062–1087 (February 2011)
Schuller, B., Wöllmer, M., Moosmayr, T., Rigoll, G.: Recognition of noisy speech: A comparative survey of robust model architecture and feature enhancement. EURASIP Journal on Audio, Speech, and Music Processing 2009, 17 (2009)
Sehr, A., Maas, R., Kellermann, W.: Reverberation model-based decoding in the logmelspec domain for robust distant-talking speech recognition. IEEE Trans. on Audio, Speech, and Lang. Process. 18(7), 1676–1691 (2010)
Wölfel, M., McDonough, J.: Distant Speech Recognition, 1st edn. Wiley, New York (2009)
Wöllmer, M., Schuller, B., Rigoll, G.: A novel Bottleneck-BLSTM front-end for feature-level context modeling in conversational speech recognition. In: Proc. of ASRU, Waikoloa, Big Island, Hawaii, pp. 36–41 (December 2011)
Wöllmer, M., Marchi, E., Squartini, S., Schuller, B.: Multi-stream LSTM-HMM decoding and histogram equalization for noise robust keyword spotting. Cognitive Neurodynamics 5(3), 253–264 (2011)
Young, S., Everman, G., Kershaw, D., Moore, G., Odell, J.: The HTK Book. Cambridge University Engineering (2006)
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Rotili, R., Principi, E., Wöllmer, M., Squartini, S., Schuller, B. (2012). Conversational Speech Recognition in Non-stationary Reverberated Environments. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds) Cognitive Behavioural Systems. Lecture Notes in Computer Science, vol 7403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34584-5_4
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DOI: https://doi.org/10.1007/978-3-642-34584-5_4
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