A.: a Tutorial on Text-independent Speaker Verification. Eurasip Journal on Applied Signal Processing, Special Issue on Biometric Signal Processing (2004) Speaker Model Speaker Recognition Engine Speaker Recognition, One to One Speaker Recognition, Overview

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【标题】A.: a Tutorial on Text-independent Speaker Verification. Eurasip Journal on Applied Signal Processing, Special Issue on Biometric Signal Processing (2004) Speaker Model Speaker Recognition Engine Speaker Recognition, One to One Speaker Recognition, Overview

【作者】 Soong  Florian Rosenberg  Merlin  T Ortega-Garcia  Reynolds  D A Quatieri  Tamsen Dunn  Richard B. Speaker  Ben  Campbell  W M Reynolds  D A Support  Jean Hennebert 

【摘要】using bottom-up clustering based on a parameter-derived distance between adapted gmms. In: ICSLP (2004) machines using GMM supervectors for speaker verification.: On the application of mixture ar hidden markov models to text-independent speaker recognition. pp. 563–570 (1991) 14. Reynolds, D., Carlson. B.: Text-dependent speaker verification using decoupled and integrated speaker and speech recognizers. Speaker model is a representation of the identity of a speaker obtained from a speech utterance of known origin. It can be generative or discriminative. Most popular generative speaker models are the Gaussian Mixture Models (GMM), which model the statistical distribution of speaker features with a mixture of Gaussians. Typical discriminative speaker models are based on the use of Support Vector Machines (SVM), where the speaker model is basically a separating hy-perplane in a high-dimensional space. Once enrolled, speaker models may be compared to a set of features coming from an utterance of unknown origin, to give a similarity score. Synonyms Voice recognition; Voice biometric 1262 S Speaker Model Definition Speaker recognition is the task of recognizing people from their voices. Speaker recognition is based on the extraction and modeling of acoustic features of speech that can differentiate individuals. These features conveys two kinds of biometric information: physiological properties (anatomical configuration of the vocal apparatus) and behavioral traits (speaking style). Automatic speaker recognition technology declines into four major tasks, speaker identification, speaker verification, speaker segmentation, and speaker tracking. While these tasks are quite different for their potential applications, the underlying technologies are yet closely related.

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