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 have achieved remarkable performance%2C largely due to the growing availability of large transcribed speech corpora. However%2C most languages lack sufficient paired speech and text data to effectively train these systems. In this article%2C 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%2C our system achieves a word error rate of nearly 20%25 without parallel transcripts or oracle word boundaries. Furthermore%2C 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.?quality=80&w=800)
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