Experiments on distant-talking speech recognition in meeting room using extended MAM

MAM has been successfully used to improve the noise robustness of speech recognizers. We apply this method in the exploration of distant-talking speech recognition for meeting room system, and propose a new extended MAM algorithm combined with LDA and MLLR techniques to compensate for additive noise and channel variation. By sharing the LDA matrix with the core recognition system, the MAM secondary model is coupled better with the intended system and can take the advantage of context speech frames. Using MAM in conjunction with model adaptation method MLLR can result in further improved recognition accuracy. Our database includes simulated 10dB additive noisy speech and real distant-talking speech by 8-channel simultaneous recording. We started from a baseline LVCSR meeting transcription system, with word error rate range from 25 to 55%. By applying extended MAM, the improved system achieves an average relative error rate reduction of 27% for 10dB additive noisy speech and 15% for real distant-talking speech.