Open AccessProceedings Article
A model-based transformational approach to robust speaker recognition.
Remco Teunen,Ben Shahshahani,Larry Heck +2 more
- pp 495-498
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TLDR
This work proposes a novel statistical modeling and compensation method for robust speaker recognition that yields similar improvements as the HNORM score-based compensation method, but with a fraction of the training time.Abstract:
A novel statistical modeling and compensation method for robust speaker recognition is presented. The method specifically addresses the degradation in speaker verification performance due to the mismatch in channels (e.g., telephone handsets) between enrollment and testing sessions. In mismatched conditions, the new approach uses speaker-independent channel transformations to synthesize a speaker model that corresponds to the channel of the testing session. Effectively verification is always performed in matched channel conditions. Results on the 1998 NIST Speaker Recognition Evaluation corpus show that the new approach yields performance that matches the best reported results. Specifically, our approach yields similar improvements (19.9% reduction in EER compared to CMN alone) as the HNORM score-based compensation method, but with a fraction of the training time.read more
Citations
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An overview of text-independent speaker recognition: From features to supervectors
Tomi Kinnunen,Haizhou Li +1 more
TL;DR: This paper starts with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling and elaborate advanced computational techniques to address robustness and session variability.
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Joint Factor Analysis Versus Eigenchannels in Speaker Recognition
TL;DR: It is shown how the two approaches to the problem of session variability in Gaussian mixture model (GMM)-based speaker verification, eigenchannels, and joint factor analysis can be implemented using essentially the same software at all stages except for the enrollment of target speakers.
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An overview of automatic speaker recognition technology
TL;DR: Some of the strengths and weaknesses of current speaker recognition technologies are discussed, and some potential future trends in research, development and applications are outlined.
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Robust Speaker Recognition in Noisy Conditions
TL;DR: This paper describes a method that combines multicondition model training and missing-feature theory to model noise with unknown temporal-spectral characteristics, and is found to achieve lower error rates.
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An Overview of Speaker Identification: Accuracy and Robustness Issues
Roberto Togneri,Daniel Pullella +1 more
TL;DR: The main paradigms for speaker identification, and recent work on missing data methods to increase robustness are presented, and combined approaches involving bottom-up estimation and top-down processing are reviewed.
References
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Journal ArticleDOI
Speaker identification and verification using Gaussian mixture speaker models
TL;DR: High performance speaker identification and verification systems based on Gaussian mixture speaker models: robust, statistically based representations of speaker identity, evaluated on four publically available speech databases.
Cepstrum analysis technique for automatic speaker verification
TL;DR: New techniques for automatic speaker verification using telephone speech based on a set of functions of time obtained from acoustic analysis of a fixed, sentence-long utterance using a new time warping method using a dynamic programming technique.
Proceedings Article
Comparison of background normalization methods for text-independent speaker verification.
TL;DR: This paper compares two approaches to background model representation for a text-independent speaker verification task using Gaussian mixture models and describes how Bayesian adaptation can be used to derive claimant speaker models, providing a structure leading to significant computational savings during recognition.
Journal ArticleDOI
Robust speaker recognition: a feature-based approach
TL;DR: Linear predictive (LP) analysis, the first step of feature extraction, is discussed, and various robust cepstral features derived from LP coefficients are described, including the afJine transform, which is a feature transformation approach that integrates mismatch to simultaneously combat both channel and noise distortion.