Maximum likelihood based HMM state filtering approach to model adaptation for long reverberation

In environment with considerably long reverberation time, each frame of speech is affected by reflected energy components from the preceding frames. Therefore to adapt parameters of a state of HMM, it becomes necessary to consider these frames, and compute their contributions to current state. However, these clean speech frames preceding to a state of HMM are not known during adaptation of the models. In this paper, we propose to use preceding states as units of preceding speech, and estimate their contributions to current state in maximum likelihood fashion. The experimental results on an isolated word recognition task showed significant improvement in performance of speech recognition system for reverberant speech, compared to other methods