A Training Criterion with Token-Level Tolerance to Transcription Ambiguity for Automatic Speech Recognition
Token-level wildcard paths in CTC cut ASR word error by tolerating localized transcript ambiguity.
The paper argues that verbatim ASR references contain localized pronunciation, spelling, and lexical differences that the acoustics do not uniquely determine. It moves Omni-temporal Classification wildcard arcs from word level to token level so unsupported tokens can be bypassed while the rest of the word stays supervised, and combines both arc types. Across 19 languages, three corpora, and 25 tasks, token-level OTC improves over CTC. A predictive-entropy schedule plus the hybrid graph gives the lowest mean word error rate on every corpus and a 9.45% average relative WER reduction versus CTC.
- Token-level wildcards bypass unsupported tokens without discarding the word.
- Token-level OTC improved over CTC on all 25 tasks.
- Hybrid graph plus entropy schedule cut relative WER 9.45% versus CTC.
- Evaluated across 19 languages and three corpora.
From the original article
1 min read at arxiv.orgAutomatic speech recognition is typically trained assuming that the reference transcript is the only valid labeling of an utterance, yet even nominally verbatim transcripts contain localized differences in pronunciation, spelling, or lexical realization that the acoustics do not uniquely determine.
By Saurabh Kumar · arxiv.org
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