Front-End Factor Analysis for Speaker Verification
Abstract
This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.
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Previously speaker recognition methods are dominated by the i-vector representation and probabilistic linear discriminant analysis framework [168], where i-vector refers to extracting low-dimensional speaker embeddings from sufficient statistics.
“Previously speaker recognition methods are dominated by the i-vector representation and probabilistic linear discriminant analysis framework [168], where i-vector refers to extracting low-dimensional speaker embeddings from sufficient statistics.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [168]
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BackgroundMultimodal 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 · see citing works
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Self-citationVoice conversion versus speaker verification: an overview
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- Authors
- Dehak, N., Kenny, P. J., Dehak, R., Dumouchel, P., & Ouellet, P.
- Affiliations
- Massachusetts Institute of Technology (US)
- Computer Research Institute of Montréal (CA)
- École Pour l'Informatique et les Techniques Avancées (FR)
- Venue
- IEEE Transactions on Audio, Speech, and Language Processing
- Published
- 2011-05
- Volume
- 19
- Issue
- 4
- Pages
- 788-798
- ISSN
- 1558-7916 (ISSN-L)
- 1558-7924
- OpenAlex
- W2150769028
- Cited by
- 3,549
- References
- 0
- Citations by year
- 2026: 64 · 2025: 122 · 2024: 167 · 2023: 215 · 2022: 239 · 2021: 335 · 2020: 353 · 2019: 348 · 2018: 334 · 2017: 304 · 2016: 264 · 2015: 222 · 2014: 265 · 2013: 154 · 2012: 100
- FWCI
- 106.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Audio Speech and Language Processing (publishedVersion)
- Funders
- Johns Hopkins University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- speaker verification, channel compensation, support vector machine, linear discriminant analysis, nuisance attribute projection, NIST Speaker Recognition Evaluation
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