DocumentCode :
3529792
Title :
Phoneme recognition using spectral envelope and modulation frequency features
Author :
Thomas, Samuel ; Ganapathy, Sriram ; Hermansky, Hynek
Author_Institution :
Idiap Res. Inst., Martigny
fYear :
2009
fDate :
19-24 April 2009
Firstpage :
4453
Lastpage :
4456
Abstract :
We present a new feature extraction technique for phoneme recognition that uses short-term spectral envelope and modulation frequency features. These features are derived from sub-band temporal envelopes of speech estimated using frequency domain linear prediction (FDLP). While spectral envelope features are obtained by the short-term integration of the sub-band envelopes, the modulation frequency components are derived from the long-term evolution of the sub-band envelopes. These features are combined at the phoneme posterior level and used as features for a hybrid HMM-ANN phoneme recognizer. For the phoneme recognition task on the TIMIT database, the proposed features show an improvement of 4.7% over the other feature extraction techniques.
Keywords :
feature extraction; hidden Markov models; neural nets; speech recognition; TIMIT database; feature extraction; frequency domain linear prediction; hybrid HMM-ANN phoneme recognizer; modulation frequency components; modulation frequency features; phoneme posterior level; phoneme recognition; short-term spectral envelope; speech estimation; sub-band temporal envelopes; Acoustics; Context modeling; Costs; Frequency modulation; Gaussian processes; Natural languages; Probability; Scalability; Speech recognition; Training data; Frequency Domain Linear Prediction; Phoneme Recognition; Spectral envelope and Modulation frequency features;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2009.4960618
Filename :
4960618
Link To Document :
بازگشت