Towards Unsupervised Speech Recognition Without Pronunciation Models
CoRR(2024)
Abstract
Recent advancements in supervised automatic speech recognition (ASR) have
achieved remarkable performance, largely due to the growing availability of
large transcribed speech corpora. However, most languages lack sufficient
paired speech and text data to effectively train these systems. In this
article, we tackle the challenge of developing ASR systems without paired
speech and text corpora by proposing the removal of reliance on a phoneme
lexicon. We explore a new research direction: word-level unsupervised ASR.
Using a curated speech corpus containing only high-frequency English words, our
system achieves a word error rate of nearly 20
oracle word boundaries. Furthermore, we experimentally demonstrate that an
unsupervised speech recognizer can emerge from joint speech-to-speech and
text-to-text masked token-infilling. This innovative model surpasses the
performance of previous unsupervised ASR models trained with direct
distribution matching.
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