Hybrid speech recognition with Deep Bidirectional LSTM
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Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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- Teacher spread
- 0.188 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to give state-of-the-art performance on the TIMIT speech database. However, the results in that work relied on recurrent-neural-network-specific objective functions, which are difficult to integrate with existing large vocabulary speech recognition systems. This paper investigates the use of DBLSTM as an acoustic model in a standard neural network-HMM hybrid system. We find that a DBLSTM-HMM hybrid gives equally good results on TIMIT as the previous work. It also outperforms both GMM and deep network benchmarks on a subset of the Wall Street Journal corpus. However the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy. We conclude that the hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates. Further investigation needs to be conducted to understand how to better leverage the improvements in frame-level accuracy towards better word error rates.
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The record
- Venue
- Topic
- Speech Recognition and Synthesis
- Field
- Computer Science
- Canadian institutions
- University of Toronto
- Funders
- —
- Keywords
- TIMITComputer scienceWord error rateSpeech recognitionLeverage (statistics)Hidden Markov modelArtificial neural networkRecurrent neural networkArtificial intelligenceDeep neural networksWord (group theory)VocabularyAcoustic modelDeep learningSpeech processing
- Has abstract in OpenAlex
- yes