VADOI:Voice-Activity-Detection Overlapping Inference For End-to-end Long-form Speech Recognition
This work addresses efficiency and accuracy trade-offs in long-form speech recognition, which is incremental as it builds on existing overlapping inference techniques.
The paper tackles the problem of performance degradation in end-to-end speech recognition for long-form sentences by proposing VADOI, which uses voice-activity detection to balance word error rate and computational cost, achieving a 20% relative computation cost reduction while maintaining WER performance compared to the best overlapping inference method.
While end-to-end models have shown great success on the Automatic Speech Recognition task, performance degrades severely when target sentences are long-form. The previous proposed methods, (partial) overlapping inference are shown to be effective on long-form decoding. For both methods, word error rate (WER) decreases monotonically when overlapping percentage decreases. Setting aside computational cost, the setup with 50% overlapping during inference can achieve the best performance. However, a lower overlapping percentage has an advantage of fast inference speed. In this paper, we first conduct comprehensive experiments comparing overlapping inference and partial overlapping inference with various configurations. We then propose Voice-Activity-Detection Overlapping Inference to provide a trade-off between WER and computation cost. Results show that the proposed method can achieve a 20% relative computation cost reduction on Librispeech and Microsoft Speech Language Translation long-form corpus while maintaining the WER performance when comparing to the best performing overlapping inference algorithm. We also propose Soft-Match to compensate for similar words mis-aligned problem.