The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
End-to-end text-dependent speaker verification
Abstract
In this paper we present a data-driven, integrated approach to speaker verification, which maps a test utterance and a few reference utterances directly to a single score for verification and jointly optimizes the system's components using the same evaluation protocol and metric as at test time. Such an approach will result in simple and efficient systems, requiring little domain-specific knowledge and making few model assumptions. We implement the idea by formulating the problem as a single neural network architecture, including the estimation of a speaker model on only a few utterances, and evaluate it on our internal "Ok Google" benchmark for text-dependent speaker verification. The proposed approach appears to be very effective for big data applications Like ours that require highly accurate, easy-to-maintain systems with a small footprint.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
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
Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
Hershey, J. R., Roux, J. L., & Weninger, F. (2014) · arXiv · doi:10.48550/arxiv.1409.2574 · Cited by 412 · cited 1×
“The proposed neural network architecture can be thought of as joint optimization of a generative-discriminative hybrid and is in the same spirit as deep unfolding [18] for adaptation.”
Background
Cited by
X-Vectors: Robust DNN Embeddings for Speaker Recognition
Snyder, D., Garcia-Romero, D., Sell, G., Povey, D., & Khudanpur, S. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461375 · Cited by 2,620
Speaker Recognition from Raw Waveform with SincNet
Ravanelli, M., & Bengio, Y. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639585 · Cited by 896
Generalized End-to-End Loss for Speaker Verification
Wan, L., Wang, Q., Papir, A., & Moreno, I. L. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462665 · Cited by 820
Self-citationAttentive Statistics Pooling for Deep Speaker Embedding
Okabe, K., Koshinaka, T., & Shinoda, K. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-993 · Cited by 514
“In fixed-phrase textdependent speaker verification, an end-to-end neural networkbased method has been proposed [6] in which Long Short-Term Memory (LSTM) with a single output from the last frame is used to obtain utterance-level speaker features, and it has outperformed conventional i-vector extraction.”
MethodTransfer learning from speaker verification to multispeaker text-to-speech synthesis
Jia, Y., Zhang, Y., Weiss, R. J., Wang, Q., Shen, J., Ren, F., Chen, Z., Nguyen, P., Pang, R., Moreno, I. L., et al. (2018) · arXiv · doi:10.48550/arxiv.1806.04558 · Cited by 519
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Metadata
- Authors
- Heigold, G., Moreno, I., Bengio, S., & Shazeer, N.
- Venue
- 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 5115-5119
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2114925438
- Cited by
- 514
- References
- 4
- FWCI
- 33.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Heigold, G., Moreno, I., Bengio, S., & Shazeer, N. (2016). End-to-end text-dependent speaker verification. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2016.7472652
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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