Speaker Separation Using Speaker Inventories and Estimated Speech

Speech separation using speaker-adapted eigenvoice speech models

We present a system for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. The sources are modeled using hidden Markov models (HMM) and separated using factorial HMM methods. Without prior speaker models for the sources in the mixture it is difficult to exactly resolve the individual sources because there is n...

Speaker separation using visual speech features and single-channel audio

This work proposes a method of single-channel speaker separation that uses visual speech information to extract a target speaker’s speech from a mixture of speakers. The method requires a single audio input and visual features extracted from the mouth region of each speaker in the mixture. The visual information from speakers is used to create a visually-derived Wiener filter. The Wiener filter...

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

Speaker Adaptation in Continuous Speech Recognition Using MLLR-Based MAP Estimation

A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...

Speaker Verification Using Coded Speech

The implementation of a pseudo text-independent Speaker Verification system is described. This system was designed to use only information extracted directly from the coded parameters embedded in the ITU-T G.729 bitstream. Experiments were performed over the YOHO database [1]. The feature vector as a short-time representation of speech consists of 16 LPC-Cepstral coefficients, as well as residu...