DocumentCode :
3424084
Title :
Context dependent quantization for distributed and/or robust speech recognition
Author :
Wan, Chia-yu ; Chen, Yi ; Lee, Lin-shan
Author_Institution :
Grad. Inst. of Commun. Eng., Nat. Taiwan Univ., Taipei
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
4413
Lastpage :
4416
Abstract :
It is well-known that the high correlation existing in speech signals is very helpful in various speech processing applications. In this paper, we propose a new concept of context-dependent quantization, in which the representative parameter (whether a scalar or a vector) for a quantization partition cell is not fixed, but depends on the signal context on both sides, and the signal context dependencies can be trained with a clean speech corpus or estimated from a noisy speech corpus. This results in a much finer quantization based on local signal characteristics, without using any extra bit rate. This approach is equally applicable to all (scalar or vector) quantization approaches, and can be used either for signal compression in distributed speech recognition (DSR) or for feature transformation in robust speech recognition. In the latter case, each feature parameter is simply transformed into its representative parameter after quantization. In preliminary experiments with AURORA 2 and simulated GPRS channels, this concept is integrated with a recently proposed histogram-based quantization (HQ), the partition cells of which are also dynamic depending on local signal statistics. Significant performance improvements were obtained with the presence of both environmental noise and transmission errors.
Keywords :
quantisation (signal); speech recognition; statistical analysis; AURORA 2; context-dependent quantization; distributed speech recognition; environmental noise; histogram-based quantization; partition cells; quantization partition cell; robust speech recognition; signal compression; signal context dependencies; signal statistics; simulated GPRS channels; speech corpus; speech processing; transmission errors; Bit rate; Context; Ground penetrating radar; Noise robustness; Quantization; Signal processing; Speech processing; Speech recognition; Statistical distributions; Working environment noise; Speech recognition; quantization; robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4518634
Filename :
4518634
Link To Document :
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