Speech recognition in a reverberant environment using matched filter array (MFA) processing and linguistic-tree maximum likelihood linear regression (LT-MLLR) adaptation

Performance of automatic speech recognition systems trained on close-talking data suffers when used in a distant-talking environment due to the mismatch in training and testing conditions. Microphone array sound capture can reduce some mismatch by removing ambient noise and reverberation but offers insufficient improvement in performance. However, using array signal capture in conjunction with Hidden Markov Model (HMM) adaptation on the clean-speech models can result in improved recognition accuracy. This paper describes an experiment in which the output of an 8-element microphone array system using MFA processing is used for speech recognition with LT-MLLR adaptation. The recognition is done in two passes. In the first pass, an HMM trained on clean data is used to recognize the speech. Using the results of this pass, the HMM model is adapted to the environment using the LT-MLLR algorithm. This adapted model, a product of MFA and LT-MLLR, results in improved recognition performance.