Acoustical and environmental robustness in automatic speech recognition
Acoustical and environmental robustness in automatic speech recognition
Cepstral parameter compensation for HMM recognition in noise
Speech Communication - Special issue on speech processing in adverse conditions
Data-driven environmental compensation for speech recognition: a unified approach
Speech Communication
On stochastic feature and model compensation approaches to robust speech recognition
Speech Communication - Special issue on robust speech recognition
An hypothesized Wiener filtering approach to noisy speech recognition
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Temporal patterns (TRAPs) in ASR of noisy speech
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Optimization of temporal filters for constructing robust features in speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
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This paper proposes several cepstral statistics compensation and normalization algorithms which alleviate the effect of additive noise on cepstral features for speech recognition. The algorithms are simple yet efficient noise reduction techniques that use online-constructed pseudo-stereo codebooks to evaluate the statistics in both clean and noisy environments. The process yields transformations for both clean speech cepstra and noise-corrupted speech cepstra, or for noise-corrupted speech cepstra only, so that the statistics of the transformed speech cepstra are similar for both environments. Experimental results show that these codebook-based algorithms can provide significant performance gains compared to results obtained by using conventional utterance-based normalization approaches. The proposed codebook-based cesptral mean and variance normalization (C-CMVN), linear least squares (LLS) and quadratic least squares (QLS) outperform utterance-based CMVN (U-CMVN) by 26.03%, 22.72% and 27.48%, respectively, in relative word error rate reduction for experiments conducted on Test Set A of the Aurora-2 digit database.