Single-channel speaker-pair identification: A new approach based on automatic frame selection

Given single-channel recordings of simultaneous speakers, we may need to identify the individual speakers for separating their voices. In this paper, we consider the problem of identifying two simultaneous speakers based on single-channel data, i.e., speakerpair identification. We model the problem as identifying speakers using noisy speech with partial temporal corruption, which corresponds to the heavily mixed speech frames. Inclusion of these noisy frames will damage the accuracy of both speakers identification. In this paper, we propose a new approach to automatically and optimally select the single-speaker dominated speech frames for identification. The new algorithm has been evaluated using two databases: 1) the GRID speech separation database and 2) the Wall Street Journal (WSJ0) database. The new approach has shown better performance than other approaches. On the Grid database, for example, the new approach outperformed the state of the art IBM approach in 5 out of 6 test conditions.