Speech Recognition with Augmented Synthesized Speech
Table 1: LibriSpeech Word Error Rate (WER) by augmentation with TTS utterances using different d-vector approaches.
['Augmentation', 'test-clean', 'test-other']
Based on the three speaker representation approaches, we generate synthesized utterances for data augmentation. Each of these experiments include a complete synthesized copy of the human speech training data. The LibriSpeech corpus contains two test (and development) partitions, one that is relatively clean (test-clean and dev-clean) and one that is not (test-other and dev-other). While this improvement is modest, the fact that performance is improved at all speaks to the coherence between the multi-speaker TTS utterances and human speech, as well as the ability for d-vector speaker representation to reasonably capture some of the acoustic diversity expressed by different speakers. For comparison, we repeated this experiment with augmentation material from high-quality single-speaker TTS using a traditional TTS frontend and WaveNet vocoder. Thus, TTS quality is a necessary but not sufficient criteria for effective data augmentation. Multi-speaker TTS is necessary for effective data augmentation for ASR.