The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Ideal ratio mask estimation using deep neural networks for robust speech recognition
Abstract
We propose a feature enhancement algorithm to improve robust automatic speech recognition (ASR). The algorithm estimates a smoothed ideal ratio mask (IRM) in the Mel frequency domain using deep neural networks and a set of time-frequency unit level features that has previously been used to estimate the ideal binary mask. The estimated IRM is used to filter out noise from a noisy Mel spectrogram before performing cepstral feature extraction for ASR. On the noisy subset of the Aurora-4 robust ASR corpus, the proposed enhancement obtains a relative improvement of over 38% in terms of word error rates using ASR models trained in clean conditions, and an improvement of over 14% when the models are trained using the multi-condition training data. In terms of instantaneous SNR estimation performance, the proposed system obtains a mean absolute error of less than 4 dB in most frequency channels.
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Cites
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On Ideal Binary Mask As the Computational Goal of Auditory Scene Analysis
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Self-citationComputational Auditory Scene Analysis
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Self-citationTowards Scaling Up Classification-Based Speech Separation
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Cited by
Supervised Speech Separation Based on Deep Learning: An Overview
Wang, D., & Chen, J. (2018) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2018.2842159 · Cited by 1,524
Self-citationA Regression Approach to Speech Enhancement Based on Deep Neural Networks
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Speech Recognition Using Deep Neural Networks: A Systematic Review
Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2896880 · Cited by 1,152
Deep Learning for Audio Signal Processing
Purwins, H., Li, B., Virtanen, T., Schluter, J., Chang, S.-Y., & Sainath, T. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2908700 · Cited by 855
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
Huang, P.-S., Kim, M., Hasegawa-Johnson, M., & Smaragdis, P. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2468583 · Cited by 461
Metadata
- Authors
- Narayanan, A., & Wang, D.
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 7092-7096
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2141411743
- Cited by
- 526
- References
- 17
- FWCI
- 24.75
- Type
- conference paper
- Language
- en
- Open access
- closed
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