Improved acoustic modeling for continuous speech recognition
HLT '90 Proceedings of the workshop on Speech and Natural Language
Automatic speech recognition and speech variability: A review
Speech Communication
Incremental HMM training applied to ECG signal analysis
Computers in Biology and Medicine
Feature compensation in the cepstral domain employing model combination
Speech Communication
Real-world acoustic event detection
Pattern Recognition Letters
Combining pulse-based features for rejecting far-field speech in a HMM-based Voice Activity Detector
Computers and Electrical Engineering
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For a speech-recognition system based on continuous-density hidden Markov models (CDHMM), speaker adaptation of the parameters of CDHMM is formulated as a Bayesian learning procedure. A speaker adaptation procedure which is easily integrated into the segmental k-means training procedure for obtaining adaptive estimates of the CDHMM parameters is presented. Some results for adapting both the mean and the diagonal covariance matrix of the Gaussian state observation densities of a CDHMM are reported. The results from tests on a 39-word English alpha-digit vocabulary in isolated word mode indicate that the speaker adaptation procedure achieves the same level of performance as that of a speaker-independent system, when one training token from each word is used to perform speaker adaptation. It shows that much better performance is achieved when two or more training tokens are used for speaker adaptation. When compared with the speaker-dependent system, it is found that the performance of speaker adaptation is always equal to or better than that of speaker-dependent training using the same amount of training data