This paper proposes a text-dependent (fixed-text) speaker verification system which uses different types of information for making a decision regarding the identity claim of a speaker. The baseline system uses the dynamic time warping (DTW) technique for matching. Detection of the end-points of an utterance is crucial for the performance of the DTW-based template matching. A method based on the vowel onset point (VOP) is proposed for locating the end-points of an utterance. The proposed method for speaker verification uses the suprasegmental and source features, besides spectral features. The suprasegmental features such as pitch and duration are extracted using the warping path information in the DTW algorithm. Features of the excitation source, extracted using the neural network models, are also used in the text-dependent speaker verification system. Although the suprasegmental and source features individually may not yield good performance, combining the evidence from these features seem to improve the performance of the system significantly. Neural network models are used to combine the evidence from multiple sources of information.

Sound units in many languages are syllabic in nature, and frequently used syllables are of consonant-vowel (CV) type. Vowel onset point (VOP) is an important event in CV units. Knowledge of VOPs helps in many applications such as speech recognition, speaker recognition, speech enhancement, begin-end detection, segmentation of speech into vowel/nonvowel-like units and finding duration of vowels. In this paper we describe parameters or features useful for manually identifying the VOPs for different types of CV units. An automatic algorithm is proposed for detecting VOPs in continuous speech, which is motivated by the nature of production and perception of speech. Speech signal is a result of exciting a time varying vocal tract system with time varying excitation. Changes in the source and system characteristics around the VOP are both useful for the detection of VOPs. In this paper we use the changes in the source characteristics for detecting the VOPs. The performance of the proposed algorithm is evaluated using 25 sentences for which a total of 236 VOPs have been identified manually. It is found that 216 VOPs have been detected within a resolution of +/− 30 ms. Compared to the energy-based approach, VOP-based begin-end detection has significantly improved the performance in the case of a text-dependent speaker verification system. For a telephone database of 32 speakers consisting of 480 genuine