Subsegmental, Segmental and Suprasegmental Features for Speaker Recognition Using Ergodic Hidden Markov Model

Subsegmental, Segmental and Suprasegmental Features for Speaker Recognition Using Gaussian Mixture Model

In the feature extraction stage, features representing speaker information are extracted from the speech signal. In the present study LP residual derived from the speech data is used for training and testing and also processing of LP residual in time domain at subsegmental, segmental and suprasegmental levels. In the training phase, GMMs are built, one for each speaker, using the training data ...

Self Determining Speaker Recognition by Three Level Segmental Processing Of Linear Prediction Residual

This paper proposes a speaker specific source information at different levels.speaker recognition system exploits the source information (LP residual) present at different levels namely subsegmental, segmental &suprasegmental. The subsegmental analysis considers LP residual in blocks of 5 msec with shift of 2.5 msec to extract speaker information. The segmental analysis extracts speaker informa...

Speaker Identification in a Shouted Talking Environment Based on Novel Third-Order Circular Suprasegmental Hidden Markov Models

It is well known that speaker identification yields very high performance in a neutral talking environment; on the other hand, the performance has been sharply declined in a shouted talking environment. This work aims at proposing, implementing, and evaluating novel Third-Order Circular Suprasegmental Hidden Markov Models (CSPHMM3s) to improve the low performance of text-independent speaker ide...

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

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