DocumentCode :
394259
Title :
Flexible feature extraction and HMM design for a hybrid distributed speech recognition system in noisy environments
Author :
Stadermann, Jan ; Rigoll, Gerhard
Author_Institution :
Inst. for Human-Machine Commun., Munich Univ. of Technol., Germany
Volume :
1
fYear :
2003
fDate :
6-10 April 2003
Abstract :
Using the client device of a distributed speech recognizer usually implies the presence of background noise since most scenarios for distributed speech recognition (DSR) are situated in a non-office environment. Thus, the general task is to choose the most suitable feature extraction method for the given conditions. We present a hybrid speech recognition approach implemented for DSR that allows the choice of arbitrary feature vectors (regarding number and range of value) without changing the amount of data sent to the recognition engine. Experiments were carried out using mel-cepstrum and RASTA-PLP features on the AURORA database. Results show how the recognition performance under different noise conditions can be adjusted if the different features are combined, and that our hybrid approach to DSR has advantages that could not that easily be obtained with traditional DSR architectures.
Keywords :
cepstral analysis; feature extraction; hidden Markov models; speech recognition; AURORA database; HMM design; RASTA-PLP features; arbitrary feature vectors; background noise; feature extraction method; flexible feature extraction; hybrid distributed speech recognition system; mel-cepstrum features; noisy environments; Background noise; Engines; Feature extraction; Hidden Markov models; Man machine systems; Network servers; Neural networks; Quantization; Speech recognition; Working environment noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-7663-3
Type :
conf
DOI :
10.1109/ICASSP.2003.1198785
Filename :
1198785
Link To Document :
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