UMA-Split: unimodal aggregation for both English and Mandarin non-autoregressive speech recognition

arXiv:2509.14653v2 Announce Type: replace Abstract: This paper proposes a unimodal aggregation (UMA) based nonautoregressive model for both English and Mandarin speech recognition. The original UMA explicitly segments and aggregates acoustic frames (with unimodal weights that first monotonically increase and then decrease) of the same text token to learn better representations than regular connectionist temporal classification (CTC). However, it only works well in Mandarin. It struggles with other languages, such as English, for which a single syllable may be tokenized into multiple fine-grain

Source: arXiv cs.CL — read the full report at the original publisher.

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