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Most of the existing modelling techniques for the speaker recognition task make an implicit assumption of sufficient data for speaker modelling and hence may lead to poor modelling under limited data condition. The present work gives an experimental evaluation of the modelling techniques like Crisp Vector Quantization (CVQ), Fuzzy Vector Quantization (FVQ), Self-Organizing Map (SOM), Learning Vector...
This paper proposes an approach for processing speech from multiple microphones to enhance speech degraded by noise and reverberation. The approach is based on exploiting the features of the excitation source in speech production. In particular, the characteristics of voiced speech can be used to derive a coherently added signal from the linear prediction (LP) residuals of the degraded speech data...
In this paper, we consider approaches for linear and nonlinear compression of feature vectors for recognition of utterances of syllable-like units in Indian languages. The distribution capturing ability of an autoassociative neural network model is exploited to derive the components for compressing the feature vectors. The nonlinear compression is accomplished by a five layer autoassociative neural...
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...
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