Multi-style training for robust speech recognition under stress

Abstract

A new training procedure has been developed to improve speech recognition performance when the talker is under stress and training tokens cannot be obtained in the recognition environment. Training tokens are obtained from the desired talker speaking both normally and with different talking styles (e.g., loud, soft, fast). The recognizer is then trained with all tokens and used under stress. This technique was tested with a, 35 aircraft-word vocabulary, 9 talker, 11 340 token, stress/style database. Two stress conditions included (1) workload stress created with a perceptual-motor critical-tracking task (2) intense noise presented through earphones at a 90 dB SPL level. A continuous-distribution speaker-dependent hidden Markov model recognizer was trained normally (five normally spoken tokens) and with multi-style training (one token each from normal, fast, clear, loud, and question-pitch talking styles). Error rates were 36% in noise and 12% under workload stress with normal training. Multi-style training reduced error rates substantially to 13% in noise and 9% under workload stress. Multi-style training also reduced the error rate for normally spoken words. [Supported by DARPA.]