Publication: Parameter Uncertainty for End-to-end Speech Recognition
Parameter Uncertainty for End-to-end Speech Recognition
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Braun, S., & Liu, S.-C. (2019, May 17). Parameter Uncertainty for End-to-end Speech Recognition. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton. https://doi.org/10.1109/icassp.2019.8683066
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Recent work on neural networks with probabilistic parameters has shown that parameter uncertainty improves network regularization. Parameter-specific signal-to-noise ratio (SNR) levels derived from parameter distributions were further found to have high correlations with task importance. However, most of these studies focus on tasks other than automatic speech recognition (ASR). This work investigates end-to-end models with probabilistic parameters for ASR. We demonstrate that probabilistic networks outperform conventional determinist
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- Braun, Stefan
- Liu, Shih-Chii
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Braun, S., & Liu, S.-C. (2019, May 17). Parameter Uncertainty for End-to-end Speech Recognition. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton. https://doi.org/10.1109/icassp.2019.8683066
