Synthesized Data Selection via Score Distribution Matching for Te Reo Māori Automatic Speech Recognition

Fine-tuning pre-trained Automatic Speech Recognition (ASR) models with synthesized datasets leads to suboptimal performance in low-resource ASR, due to domain mismatches between true and synthesized speech data. To address this issue, we propose a novel score-distribution-matching method for data selection, which filters synthesized data by explicitly aligning its score distribution with the prior distribution of the true data score. We apply our proposed method on the ASR of Te Reo Māori (the Māori langauge), an official yet endangered language of New Zealand. We legitimately collect and label 27 hours of Te Reo Māori speech data and synthesize an additional 520 hours using our recently developed Zero-Voice model, a zero-shot text-to-speech (TTS) system. By fine-tuning the pre-trained ASR model Whisper-large-v3 using both true and filtered synthesized data selected by our proposed approach, we achieve a remarkable improvement in accuracy for Te Reo Māori ASR that outperforms baseline models on the FLEURS Māori (mi_nz) benchmark.