An E2E ASR model with mixed wordpieces and phonemes improves foreign proper noun recognition via phoneme-level contextual biasing, showing 16% gain over grapheme-only and 8% over wordpiece-only baselines.
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. We model the output vocabulary of about 100,000 words directly using deep bi-directional LSTM RNNs with CTC loss. The model is trained on 125,000 hours of semi-supervised acoustic training data, which enables us to alleviate the data sparsity problem for word models. We show that the CTC word models work very well as an end-to-end all-neural speech recognition model without the use of traditional context-dependent sub-word phone units that require a pronunciation lexicon, and without any language model removing the need to decode. We demonstrate that the CTC word models perform better than a strong, more complex, state-of-the-art baseline with sub-word units.
years
2019 2verdicts
UNVERDICTED 2representative citing papers
3D-2D-CNN-BLSTM with word-CTC reaches 1.3% WER on GRID seen-speaker lipreading (55% relative gain over LCANet) and 8.6% on unseen speakers (24.5% gain over LipNet).
citing papers explorer
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Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models
An E2E ASR model with mixed wordpieces and phonemes improves foreign proper noun recognition via phoneme-level contextual biasing, showing 16% gain over grapheme-only and 8% over wordpiece-only baselines.
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LipReading with 3D-2D-CNN BLSTM-HMM and word-CTC models
3D-2D-CNN-BLSTM with word-CTC reaches 1.3% WER on GRID seen-speaker lipreading (55% relative gain over LCANet) and 8.6% on unseen speakers (24.5% gain over LipNet).