Subband analysis for robust speech recognition in the presence of car noise
Çetin, A. Enis
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1995
417 - 420
Item Usage Stats
MetadataShow full item record
In this paper, a new set of speech feature representations for robust speech recognition in the presence of car noise are proposed. These parameters are based on subband analysis of the speech signal. Line Spectral Frequency (LSF) representation of the Linear Prediction (LP) analysis in subbands and cepstral coefficients derived from subband analysis (SUBCEP) are introduced, and the performances of the new feature representations are compared to mel scale cepstral coefficients (MELCEP) in the presence of car noise. Subband analysis based parameters are observed to be more robust than the commonly employed MELCEP representations.
Pattern recognition systems
Line spectral frequency
Linear prediction analysis
Mel scale cepstral coefficient
Hidden Markov model
Linear predictive coding
Mel scale cepstral coefficients
Speech recognition system
Published Version (Please cite this version)https://doi.org/10.1109/ICASSP.1995.479610
Showing items related by title, author, creator and subject.
Shriberg, E.; Stolcke, A.; Hakkani-Tür, D.; Tür, G. (Elsevier, 2000)A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues ...
Erzin, E.; Çetin, A. E. (IEEE, 1994)Line spectrum frequencies (LSF's) uniquely represent the linear predictive coding (LPC) filter of a speech frame. In many vocoders LSF's are used to encode the LPC parameters. In this paper, an inter-frame differential ...
Erzin, Engin; Çetin, A. Enis (IEEE, 1993-04)Line Spectrum Frequencies (LSF's) uniquely represent the Linear Predictive Coding (LPC) filter of a speech frame. In many vocoders LSF's are used to encode the LPC parameters. In this paper, an interframe differential ...