Speaker Recognition in the Text-Independent Domain Using Keyword Hidden Markov Models

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

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

Text-constrained speaker recognition on a text-independent task

We present an approach to speaker recognition in the textindependent domain of conversational telephone speech using a text-constrained system designed to employ select highfrequency keywords in the speech stream. The system uses speaker word models generated via Hidden Markov Models (HMMs) — a departure from the traditional Gaussian Mixture Model (GMM) approach dominant in text-independent wor...

Speaker Recognition using keyword Hidden Markov Models and Support vector machines

New approaches to speaker and background model training have given rise to many recent developments in speaker recognition. Recently, various text-dependent approaches have surfaced, including a keyword Hidden Markov Models (HMM) approach [1]. This approach also deviates from the traditional bag-offrames approach by taking into account relationships in time among acoustic features for different...

Text-constrained Speaker Recognition Using Hidden Markov Models

This paper presents a possible application of a text-dependent speaker recognition system within the unconstrained domain of telephone conversation speech, as contained in the Switchboard I corpus. The system utilizes word HMMs to generate likelihood scores for key words among the backchannel, filled pause, and discourse marker categories. Results on tests using a variant of the NIST 2001 exten...