Speaker Normalization using Gaussian Mixture Model for Speaker Independent Speech Recognition

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Across-speaker articulatory normalization for speaker-independent silent speech recognition

Silent speech interfaces (SSIs), which recognize speech from articulatory information (i.e., without using audio information), have the potential to enable persons with laryngectomy or a neurological disease to produce synthesized speech with a natural sounding voice using their tongue and lips. Current approaches to SSIs have largely relied on speaker-dependent recognition models to minimize t...

Speaker Recognition System using Gaussian Mixture Model

In this paper,features for text-independent speaker recognition has been evaluated. Speaker identification from a set of templates and analyzing speaker recognition rate by extracting several key features like Mel Frequency Cepstral Coefficients [MFCC] from the speech signals of those persons by using the process of feature extraction using MATLAB2013 .These features are effectively captured us...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Robust text-independent speaker identification using Gaussian mixture speaker models

This paper introduces and motivates the use of Gaussian mixture models (CMM) 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 efTective for modeling speaker identity. The focus of this work is on applications which require high identification rates using short utterance ...