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Segmental models are sequence prediction models in which scores of hypotheses are based on entire variable-length segments of frames. We consider segmental models for whole-word ("acoustic-to-word") speech recognition, with the feature vectors defined using vector embeddings of segments. Such models are computationally challenging as the number of paths is proportional to the vocabulary size, which can be orders of magnitude larger than when using subword units like phones. We describe an efficient approach for end-to-end whole-word segmental models, with forward-backward and Viterbi decoding performed on a GPU and a simple segment scoring function that reduces space complexity. In addition, we investigate the use of pre-training via jointly trained acoustic word embeddings (AWEs) and acoustically grounded word embeddings (AGWEs) of written word labels. We find that word error rate can be reduced by a large margin by pre-training the acoustic segment representation with AWEs, and additional (smaller) gains can be obtained by pre-training the word prediction layer with AGWEs. Our final models improve over prior A2W models.
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This content will become publicly available on August 1, 2026
Tracking the dynamic word-by-word incremental reading through multimeasures.
- Award ID(s):
- 2118195
- PAR ID:
- 10632131
- Publisher / Repository:
- American Psychological Association
- Date Published:
- Journal Name:
- Journal of Experimental Psychology: Learning, Memory, and Cognition
- Volume:
- 51
- Issue:
- 8
- ISSN:
- 0278-7393
- Page Range / eLocation ID:
- 1324 to 1346
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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