An on-line acoustic compensation technique for robust speech recognition
Diego Giuliani · 1999
Feature vector normalization has been successfully used to improve the noise robustness of speech recognizers. Unfortunately, it may cause additional insertion errors in connected digit recognition in clean environments. We propose two methods to reduce the number of insertions. Based on estimated instantaneous signal-to-noise ratio we form a reliability measure for the recognized digits. We discard unreliable digits from the beginning and the end of the recognized digit sequence. Since the proposed reliability hypotheses are independent of the likelihoods produced by an HMM classifier, we are capable of bringing new useful information into the classification process. In addition, we constrain the normalization process on the basis of statistics obtained from the training data. Experimental results show that we are capable of achieving an average 32% string level error rate reduction in simulations of a noisy car environment.