Investigation of Practical Aspects of Single Channel Speech Separation for ASR

Understand

Speech separation has been successfully applied as a frontend processing module of conversation transcription systems thanks to its ability to handle overlapped speech and its flexibility to combine with downstream tasks such as automatic speech recognition (ASR).

• However, a speech separation model often introduces target speech distortion, resulting in a sub-optimum word error rate (WER).
• In this paper, we describe our efforts to improve the performance of a single channel speech separation system.
• Specifically, we investigate a two-stage training scheme that firstly applies a feature level optimization criterion for pretraining, followed by an ASR-oriented optimization criterion using an end-to-end (E2E) speech recognition model.