Adaptive co-channel speech separation and recognition
Kuan-Chieh Yen, Yunxin Zhao · IEEE Transactions on Speech and Audio Processing · 1999
An improved technique of co-channel speech separation, S-AADP/LMS, and its integration with automatic speech recognition is presented. The S-AADF/LMS technique is based on the algorithms of accelerated adaptive decorrelation filtering (AADP) and LMS noise cancellation, where a switching between the two algorithms is made depending upon the active/inactive status of the co-channel signal sources. The AADF improves the previous adaptive decorrelation algorithm in terms of system stability and estimation efficiency, and leads to better estimation of time-varying and reverberant channels. The S-AADF/LMS further improves the estimation accuracy when only one source signal remains active during certain periods of time. A coherence-function based source signal detection algorithm is also presented, which is successfully used in the switching between AADF and LMS and in extracting speech signals from leakage-corrupted background. Experiments were conducted under a simulated environment based on the measurements made of certain real room-acoustic conditions, and the results demonstrated the effectiveness of the proposed technique for co-channel speech separation and recognition.