The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Speaker adaptation of neural network acoustic models using i-vectors
Abstract
We propose to adapt deep neural network (DNN) acoustic models to a target speaker by supplying speaker identity vectors (i-vectors) as input features to the network in parallel with the regular acoustic features for ASR. For both training and test, the i-vector for a given speaker is concatenated to every frame belonging to that speaker and changes across different speakers. Experimental results on a Switchboard 300 hours corpus show that DNNs trained on speaker independent features and i-vectors achieve a 10% relative improvement in word error rate (WER) over networks trained on speaker independent features only. These networks are comparable in performance to DNNs trained on speaker-adapted features (with VTLN and FMLLR) with the advantage that only one decoding pass is needed. Furthermore, networks trained on speaker-adapted features and i-vectors achieve a 5-6% relative improvement in WER after hessian-free sequence training over networks trained on speaker-adapted features only.
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Cites
Front-End Factor Analysis for Speaker Verification
Dehak, N., Kenny, P. J., Dehak, R., Dumouchel, P., & Ouellet, P. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2064307 · Cited by 3,549
An investigation of deep neural networks for noise robust speech recognition
Seltzer, M. L., Yu, D., & Wang, Y. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639100 · Cited by 640
Seide, F., Li, G., Chen, X., & Yu, D. (2011) · 2011 IEEE Workshop on Automatic Speech Recognition & Understanding · doi:10.1109/asru.2011.6163899 · Cited by 632
Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets
Sainath, T. N., Kingsbury, B., Sindhwani, V., Arisoy, E., & Ramabhadran, B. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638949 · Cited by 593
Language recognition via i-vectors and dimensionality reduction
Dehak, N., Torres-Carrasquillo, P. A., Reynolds, D., & Dehak, R. (2011) · Interspeech 2011 · doi:10.21437/interspeech.2011-328 · Cited by 419
Yu, D., Yao, K., Su, H., Li, G., & Seide, F. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639201 · Cited by 408
Joint Factor Analysis of Speaker and Session Variability: Theory and Algorithms
Kenny, P. J. (2006) · Cited by 376
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Self-citationAbdel-Hamid, O., & Jiang, H. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639211 · Cited by 207
Adaptation of context-dependent deep neural networks for automatic speech recognition
Yao, K., Yu, D., Seide, F., Su, H., Deng, L., & Gong, Y. (2012) · 2012 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2012.6424251 · Cited by 194
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Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
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Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2896880 · Cited by 1,152
Multimodal intelligence: Representation learning, information fusion, and applications
Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511
The Microsoft 2017 Conversational Speech Recognition System
Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., & Stolcke, A. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461870 · Cited by 472
Metadata
- Authors
- Saon, G., Soltau, H., Nahamoo, D., & Picheny, M.
- Venue
- 2013 IEEE Workshop on Automatic Speech Recognition and Understanding
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 55-59
- OpenAlex
- W2079623482
- Cited by
- 577
- References
- 14
- FWCI
- 41.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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