Abstract:

The recently-proposed deep clustering algorithm introduced significant advances in monaural speaker-independent multi-speaker speech separation. Deep clustering operates on magnitude spectrograms using bidirectional recurrent networks and K-means clustering, both of which require offline operation, i.e., algorithm latency is longer than utterance length. This paper evaluates architectures for reduced latency deep clustering by combining: (1) block processing to efficiently propagate the memory encoded by the recurrent network, and (2) teacher-student learning, where low-latency models learn from an offline teacher. Compared to our best performing offline model, we only lose 0.3 dB SDR at a latency of 1.2 seconds and 0.7 dB SDR at a latency of 0.6 seconds on the publicly available wsj0-2mix dataset. Moreover, by providing a detailed analysis of the failure cases for our low-latency speech separation models, we show that the cause of this performance gap is related to frame-level permutation errors, where the network fails to accurately track speaker identity throughout an utterance.