The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Analysis of i-vector length normalization in speaker recognition systems
Abstract
We present a method to boost the performance of probabilistic generative models that work with i-vector representations. The proposed approach deals with the nonGaussian behavior of i-vectors by performing a simple length normalization. This non-linear transformation allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions. Significant performance improvements are demonstrated on the telephone portion of NIST SRE 2010.
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Citations
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
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
Self-citationSpeaker Recognition by Machines and Humans: A tutorial review
Hansen, J. H. L., & Hasan, T. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2462851 · Cited by 621
Spoofing and countermeasures for speaker verification: A survey
Wu, Z., Evans, N., Kinnunen, T., Yamagishi, J., Alegre, F., & Li, H. (2015) · Speech Communication · doi:10.1016/j.specom.2014.10.005 · Cited by 601
Attentive Statistics Pooling for Deep Speaker Embedding
Okabe, K., Koshinaka, T., & Shinoda, K. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-993 · Cited by 514
“Mean subtraction, whitening, and length normalization [17] were applied to the i-vector as pre-processing steps before sending it to the PLDA, and similarity was then evaluated using a PLDA model with a speaker space of 400 dimensions.”
MethodSpeechBrain: A General-Purpose Speech Toolkit
Ravanelli, M., Parcollet, T., Plantinga, P., Rouhe, A., Cornell, S., Lugosch, L., Subakan, C., Dawalatabad, N., Heba, A., Zhong, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.04624 · Cited by 512
Metadata
- Authors
- Garcia-Romero, D., & Espy-Wilson, C. Y.
- Venue
- Interspeech 2011
- Publisher
- ISCA
- Published
- 2011-08-27
- Pages
- 249-252
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2395750323
- Cited by
- 922
- References
- 1
- FWCI
- 50.50
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Garcia-Romero, D., & Espy-Wilson, C. Y. (2011). Analysis of i-vector length normalization in speaker recognition systems. Interspeech 2011. https://doi.org/10.21437/interspeech.2011-53
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