How does the inclusion of explicit phoneme alignment in UniSpeech-derived representations affect phoneme error

Abstract

Abstract: UniSpeech has achieved superior performance in cross-lingual automatic speech recognition (ASR) by explicitly aligning latent representations to phoneme units using multi-task self-supervised learning. While the learned representations transfer well from high-resource to low-resource languages, predicting words directly from these phonetic representations in downstream ASR is challenging. In this paper, we propose TranUSR, a two-stage model comprising a pre-trained UniData2vec and a phoneme-to-word Transcoder. Different from UniSpeech, UniData2vec replaces the quantized discrete representation

Research Question

How does the inclusion of explicit phoneme alignment in UniSpeech-derived representations affect phoneme error rate (PER) and word error rate (WER) in cross-lingual zero-shot transfer learning compared to multilingual wav2vec 2.0 on LibriLight and MLS benchmarks?

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