To improve the reliability of telephone-based speaker verification systems, channel com-pensation is indispensable. However, it is also important to ensure that the channel com-pensation algorithms in these systems surpress channel variations and enhance interspeaker distinction. This paper addresses this problem by a blind feature-based transformation ap-proach in which the transformation parameters are determined online without any a priori knowledge of channel characteristics. Specifically, a composite statistical model formed by the fusion of a speaker model and a background model is used to represent the characteristics of enrollment speech. Based on the difference between the claimant’s speech and the com-posite model, a stochastic matching type of approach is proposed to transform the claimant’s speech to a region close to the enrollment speech. Therefore, the algorithm can estimate the transformation online without the necessity of detecting the handset types. Experimental results based on the 2001 NIST evaluation set show that the proposed transformation ap-proach achieves significant improvement in both equal error rate and minimum detection cost as compared to cepstral mean subtraction, Znorm, and short-time Gaussianization.

This paper proposes a constrained stochastic feature transformation algorithm for robust speaker verification. The algorithm computes the feature transformation parameters based on the statistical difference between a test utterance and a composite GMM formed by combining the speaker and background models. The transformation is then used to transform the test utterance to fit the clean speaker model and background model before verification. By implicitly constraining the transformation, the transformed features can fit both models simultaneously. Experimental results based on the 2001 NIST evaluation set show that the proposed algorithms achieves significant improvement in both equal error rate and minimum detection cost when compared to cepstral mean subtraction and Z-norm. The performance of the proposed transformation approach is also slightly better than the short-time Gaussianization method proposed in [1].

Given a speech signal there are two kinds of information that may be extracted from it. On one hand there is the linguistic information about what is being said, and on the other there is also speaker specific information. This report deals with the task of speaker recognition where the goal is to determine which one of a group known speakers best matches the input voice sample. The problem is made harder when the speakers are not constrained to a particular word sequence,when there is only a very small amount of train and test data, or when the train and test data are collected across different channels. In this

This paper investigates the problem of speaker identification and verification in noisy conditions, assuming that speech signals are corrupted by environmental noise but knowledge about the noise characteristics is not available. This research is motivated in part by the potential application of speaker recognition technologies on handheld devices or the Internet. While the technologies promise an additional biometric layer of security to protect the user, the practical implementation of such systems faces many challenges. One of these is environmental noise. Due to the mobile nature of such systems, the noise sources can be highly time-varying and potentially unknown. This raises the requirement for noise robustness in the absence of information of the noise. This paper describes a method, named universal compensation (UC), that combines multi-condition training and the missing-feature method to model noises with unknown temporal-spectral characteristics. Multi-condition training is conducted using simulated noisy data with limited noise varieties, providing a “coarse ” compensation for the noise, and the missing-feature method refines the compensation by ignoring noise variations outside the given training conditions, thereby reducing the training and testing mismatch. This paper is focused on several issues relating to the implementation of the UC model for real-world applications. These include the generation