Enhancement and modification of automatic speaker verification by utilizing hidden Markov model

Transition-oriented hidden Markov models for speaker verification

In this article, we present a novel mechanism by which more precise voiceprints can be constructed in a typical text-dependent speaker veri cation system based on a continuous density hidden Markov model (HMM). Typical voiceprints (speaker-dependent HMMs) are rst trained using a subscriber's enrollment data. The resulting models are then restructured to permit a modeling of sub-state behavior. ...

An Automatic Retraining Method for Speaker Independent Hidden Markov Models

When training speaker-independent HMM-based acoustic models, a lot of manually transcribed acoustic training data must be available from a good many different speakers. These training databases have a great variation in the pitch of the speakers, articulation and the speed of talking. In practice, the speaker-independent models are used for bootstrapping the speaker-dependent models built by sp...

Hidden-Markov-Model Based Speech Enhancement

The goal of this contribution is to use a parametric speech synthesis system for reducing background noise and other interferences from recorded speech signals. In a first step, Hidden Markov Models of the synthesis system are trained. Two adequate training corpora consisting of text and corresponding speech files have been set up and cleared of various faults, including inaudible utterances or...

Automatic Word Spacing Using Hidden Markov Model

Speech enhancement based on hidden Markov model using sparse code shrinkage

This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...