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Improving child speech recognition with augmented child-like speech

Yuanyuan Zhang·Zhengjun Yue·Tanvina Patel·Odette Scharenborg
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Abstract

State-of-the-art ASRs show suboptimal performance for child speech. The scarcity of child speech limits the development of child speech recognition (CSR). Therefore, we studied child-to-child voice conversion (VC) from existing child speakers in the dataset and additional (new) child speakers via monolingual and cross-lingual (Dutch-to-German) VC, respectively. The results showed that cross-lingual child-to-child VC significantly improved child ASR performance. Experiments on the impact of the quantity of child-to-child cross-lingual VC-generated data on fine-tuning (FT) ASR models gave the best results with two-fold augmentation for our FT-Conformer model and FT-Whisper model which reduced WERs with ~3% absolute compared to the baseline, and with six-fold augmentation for the model trained from scratch, which improved by an absolute 3.6% WER. Moreover, using a small amount of "high-quality" VC-generated data achieved similar results to those of our best-FT models.

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Yuanyuan Zhang, Zhengjun Yue, Tanvina Patel, Odette Scharenborg. 2024-06-12. Improving child speech recognition with augmented child-like speech. https://doi.org/10.21437/interspeech.2024-485

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