Error correction for speaker-independent isolated word recognition through likelihood compensation using phonetic bigram

Phonetic speaker recognition using maximum-likelihood binary-decision tree models

Recent work in phonetic speaker recognition has shown that modeling phone sequences using n-grams is a viable and effective approach to speaker recognition, primarily aiming at capturing speaker-dependent pronunciation and also word usage. This paper describes a method involving binary-tree-structured statistical models for extending the phonetic context beyond that of standard n-grams (particu...

Speech recognition using sub-word units dependent on phonetic contexts of both training and recognition vocabularies

This paper proposes a new speech recognition algorithm using a new context-dependent recognition unit design method for e cient and precise acoustic modeling. This algorithm uses both training and recognition vocabularies to select context-dependent units which precisely represent acoustic variations due to phonetic contexts in a recognition vocabulary. An e cient training algorithm for selecte...

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

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

A fast maximum likelihood nonlinear feature transformation method for GMM-HMM speaker adaptation

We describe a novel maximum likelihood nonlinear feature bias compensation method for Gaussian mixture model–hidden Markov model (GMM–HMM) adaptation. Our approach exploits a single-hiddenlayer neural network (SHLNN) that, similar to the extreme learning machine (ELM), uses randomly generated lower-layer weights and linear output units. Different from the conventional ELM, however, our approach...