On the impact of signal preprocessing for robust distant speech recognition in adverse acoustic environments

Reindl K, Zheng Y, Meier S, Schwarz A, Kellermann W (2012)


Publication Language: English

Publication Type: Conference contribution

Publication year: 2012

City/Town: Hong Kong, China

Pages Range: 131-135

Event location: Hong Kong CN

ISBN: 978-1-4673-2193-8

DOI: 10.1109/ICSPCC.2012.6335732

Abstract

In this contribution, a two-channel acoustic front-end for robust automatic speech recognition (ASR) in adverse acoustic environments is analyzed. The source signal extraction scheme combines a blocking matrix based on semi-blind source separation, which provides a continuously updated reference of all undesired components separated from the desired signal and its reflections, and a single-channel Wiener postfilter. The postfilter is directly derived from the obtained noise and interference reference signal and hence, generalizes well-known postfilter realizations. The proposed front-end and its integration into an ASR system are analyzed and evaluated with respect to keyword accuracy under reverberant conditions with unpredictable and nonstationary interferences, and for different target source distances. Evaluating a simplified front-end based on free-field assumptions, an ideal front-end, where knowledge about the true undesired components is assumed, and comparing the proposed scheme with the competitive approach of solely using multistyle training, demonstrates the importance of an adequate signal preprocessing for robust distant speech recognition.

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How to cite

APA:

Reindl, K., Zheng, Y., Meier, S., Schwarz, A., & Kellermann, W. (2012). On the impact of signal preprocessing for robust distant speech recognition in adverse acoustic environments. In Proceedings of the IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 131-135). Hong Kong, CN: Hong Kong, China.

MLA:

Reindl, Klaus, et al. "On the impact of signal preprocessing for robust distant speech recognition in adverse acoustic environments." Proceedings of the IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Hong Kong Hong Kong, China, 2012. 131-135.

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