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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Acoustically grounded word embeddings for improved acoustics-to-word speech recognition
Direct acoustics-to-word (A2W) systems for end-to-end automatic
speech recognition are simpler to train, and more efficient to decode
with, than sub-word systems. However, A2W systems can have difficulties at training time when data is limited, and at decoding time
when recognizing words outside the training vocabulary. To address
these shortcomings, we investigate the use of recently proposed acoustic and acoustically grounded word embedding techniques in A2W
systems. The idea is based on treating the final pre-softmax weight
matrix of an AWE recognizer as a matrix of word embedding vectors,
and using an externally trained set of word embeddings to improve
the quality of this matrix. In particular we introduce two ideas: (1)
Enforcing similarity at training time between the external embeddings
and the recognizer weights, and (2) using the word embeddings at
test time for predicting out-of-vocabulary words. Our word embedding model is acoustically grounded, that is it is learned jointly with
acoustic embeddings so as to encode the words’ acoustic-phonetic
content; and it is parametric, so that it can embed any arbitrary (potentially out-of-vocabulary) sequence of characters. We find that
both techniques improve the performance of an A2W recognizer on
conversational telephone speech.
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- Award ID(s):
- 1816627
- NSF-PAR ID:
- 10109237
- Date Published:
- Journal Name:
- ICASSP 2019
- ISSN:
- 0736-7791
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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