Acoustic-to-Word recognition provides a straightforward solution to end-to-end speech recognition without needing external decoding, language model re-scoring or lexicon. While character-based models offer a natural solution to the out-ofvocabulary problem, word models can be simpler to decode and may also be able to directly recognize semantically meaningful units. We present effective methods to train Sequenceto-Sequence models for direct word-level recognition (and character-level recognition) and show an absolute improvement of 4.4-5.0% in Word Error Rate on the Switchboard corpus compared to prior work. In addition to these promising results, word-based models are more interpretable than character models, which have to be composed into words using a separate decoding step. We analyze the encoder hidden states and the attention behavior, and show that locationaware attention naturally represents words as a single speechword-vector, despite spanning multiple frames in the input. We finally show that the Acoustic-to-Word model also learns to segment speech into words with a mean standard deviation of 3 frames as compared with human annotated forcedalignments for the Switchboard corpus.
Using a supervised end-to-end speech recognition model, REF learn acoustic word embeddings and how to segment speech into words.
Acoustic-to-Word Recognition with Sequence-to-Sequence Models
2018 IEEE Spoken Language Technology Workshop (SLT)
2018 IEEE Spoken Language Technology Workshop (SLT)
Computer Science Engineering