This paper integrates location information of frames into conventional acoustic model （AM） and language model （LM） likelihoods, in order to distinguish potential path can- didates more precisely at decoding stage. This paper proposes an induced probability, which represents location information of frames within the whole acoustic space. By integrating the induced probability, the decoder is directed to search within the most promising regions of acoustic space. Promising paths are enhanced and unlikely paths are weakened. Experiments conducted on Chinese Putonghua show that the character error rate is reduced by 10.95% rel- atively without increasing decoding complexity significantly. Finally, pruning analysis shows that integrating location information of frames into traditional decoding framework is helpful for improving system performance.
Although the signal subspace approach has been studied extensively for speech enhancement, no good solution has been found to identify signal subspace dimension in multi- channel situation. This paper presents a signal subspace dimension estimator based on F-norm of correlation matrix, with which subspace-based multi-channel speech enhancement is robust to adverse acoustic environments such as room reverberation and low input signal to noise ratio （SNR）. Experiments demonstrate the presented method leads to more noise reduction and less speech distortion comparing with traditional methods.
