Usable Speech Assignment for Speaker Identification under Co-Channel Situation
This work addresses speaker identification in co-channel speech, which is an incremental improvement for audio processing applications.
The paper tackles the problem of organizing usable speech segments for speaker identification in co-channel speech by developing a model-based speaker assignment method using posterior probability and exhaustive search, resulting in a significant improvement as evaluated on the TIMIT database.
Usable speech criteria are proposed to extract minimally corrupted speech for speaker identification (SID) in co-channel speech. In co-channel speech, either speaker can randomly appear as the stronger speaker or the weaker one at a time. Hence, the extracted usable segments are separated in time and need to be organized into speaker streams for SID. In this paper, we focus to organize extracted usable speech segment into a single stream for the same speaker by speaker assignment system. For this, we develop model-based speaker assignment method based on posterior probability and exhaustive search algorithm. Evaluation of this method is performed on TIMIT database. The system is evaluated on co-channel speech and results show a significant improvement.