A Supervised Text-Independent Speaker Recognition Approach

Connectionist speaker normalization and adaptation

In a speaker-independent, large-vocabulary continuous speech recognition systems, recognition accuracy varies considerably from speaker to speaker, and performance may be significantly degraded for outlier speakers such as nonnative talkers. In this paper, we explore supervised speaker adaptation and normalization in the MLP component of a hybrid hidden Markov model/ multilayer perceptron versi...

Denoising autoencoder-based speaker feature restoration for utterances of short duration

This paper describes a speaker feature restoration method for improving text-independent speaker recognition with short utterances. The method employs a denoising autoencoder (DAE) to compensate speaker features of a short utterance which contains limited phonetic information. It first estimates phonetic distribution in the utterance as posteriors based on speech models and then transforms an i...

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...

Text independent speaker recognition using speaker dependent word spotting

This paper is motivated by the fact that text dependent speaker recognition is inherently more accurate than text independent speaker recognition. In this work we assign models to frequent words spoken by a speaker and spot them in a test call. In this way, text-dependent speaker recognition technology can be used for text independent tasks. The approach we take is to use DTW (Dynamic Time Warp...

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