This paper introduces and motivates the use of Gaussian mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity. The focus of this work is on applications which require high identification rates using short utterance from unconstrained conversational speech and robustness to degradations produced by transmission over a telephone channel. A complete experimental evaluation of the Gaussian mixture speaker model is conducted on a 49 speaker, conversational telephone speech database. The experiments examine algorithmic issues (initialization, variance limiting, model order selection), spectral variability robustness techniques, large population performance, and comparisons to other speaker modeling techniques (uni-modal Gaussian, VQ codebook, tied Gaussian mixture, and radial basis functions). The Gaussian mixture speaker model attains 96.8% identification accuracy using 5 second clean speech utterances and 80.8% accuracy using 15 second telephone speech utterances with a 49 speaker population and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task. >

Robust text-independent speaker identification using Gaussian mixture speaker models

Survey on speech emotion recognition: Features, classification schemes, and databases

A tutorial on the design and development of automatic speaker-recognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person's claimed identity. Speech processing and the basic components of automatic speaker-recognition systems are shown and design tradeoffs are discussed. Then, a new automatic speaker-recognition system is given. This recognizer performs with 98.9% correct decalcification. Last, the performances of various systems are compared.

Speaker recognition: a tutorial

http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

Cepstrum analysis technique for automatic speaker verification

Several different parametric representations of speech derived from the linear prediction model are examined for their effectiveness for automatic recognition of speakers from their voices. Twelve predictor coefficients were determined approximately once every 50 msec from speech sampled at 10 kHz. The predictor coefficients and other speech parameters derived from them, such as the impulse response function, the autocorrelation function, the area function, and the cepstrum function were used as input to an automatic speaker‐recognition system. The speech data consisted of 60 utterances, consisting of six repetitions of the same sentence spoken by 10 speakers. The identification decision was based on the distance of the test sample vector from the reference vector for different speakers in the population; the speaker corresponding to the reference vector with the smallest distance was judged to be the unknown speaker. In verification, the speaker was verified if the distance between the test sample vector and the reference vector for the claimed speaker was less than a fixed threshold. Among all the parameters investigated, the cepstrum was found to be the most effective, providing an identification accuracy of 70% for speech 50 msec in duration, which increased to more than 98% for a duration of 0.5 sec. Using the same speech data, the verification accuracy was found to be approximately 83% for a duration of 50 msec, increasing to 98% for a duration of 1 sec. In a separate study to determine the feasibility of text‐independent speaker identification, an identification accuracy of 93% was achieved for speech 2 sec in duration even though the texts of the test and reference samples were different.

B. S. Atal

Papers

Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification