Journal ArticleDOI
A genetic classification method for speaker recognition
Qingyang Hong,Sam Kwong +1 more
TLDR
A hybrid training method based on genetic algorithm (GA) that utilizes the global searching capability of GA and combines the effectiveness of the ML method is proposed.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2005-02-01. It has received 42 citations till now. The article focuses on the topics: Speaker recognition & TIMIT.read more
Citations
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Journal ArticleDOI
Multi-agent decision fusion for motor fault diagnosis
TL;DR: In this article, a decision fusion system for fault diagnosis, which integrates data sources from different types of sensors and decisions of multiple classifiers, is proposed, which can lead to super performance when compared with the best individual classifier with single-source data.
Multi-agent decision fusion for motor fault diagnosis
TL;DR: A decision fusion system for fault diagnosis, which integrates data sources from different types of sensors and decisions of multiple classifiers, and multi-agent classifiers fusion algorithm is employed as the core of the whole fault diagnosis system.
Journal ArticleDOI
Automated speech analysis applied to laryngeal disease categorization
TL;DR: The effectiveness of 11 different feature sets in classification of voice recordings of the sustained phonation of the vowel sound /a/ into a healthy and two pathological classes, diffuse and nodular, is investigated.
Journal ArticleDOI
Categorizing normal and pathological voices: automated and perceptual categorization.
Virgilijus Uloza,Antanas Verikas,Antanas Verikas,Marija Bacauskiene,Adas Gelzinis,Ruta Pribuisiene,Marius Kaseta,Viktoras Šaferis +7 more
TL;DR: An elaborated automated voice categorization system that classified voice signal samples into healthy and pathological classes and to compare it with classification accuracy that was attained by human experts was evaluated.
Journal ArticleDOI
An Efficient Digital VLSI Implementation of Gaussian Mixture Models-Based Classifier
Minghua Shi,Amine Bermak +1 more
TL;DR: A number of design strategies are proposed in order to achieve the best possible tradeoffs between circuit complexity and real-time processing in GMM and its hardware complexity is analyzed and compared with a number of benchmark algorithms.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Fundamentals of speech recognition
TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
Journal ArticleDOI
Robust text-independent speaker identification using Gaussian mixture speaker models
Douglas A. Reynolds,Richard Rose +1 more
TL;DR: The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task.
Journal ArticleDOI
Speaker identification and verification using Gaussian mixture speaker models
TL;DR: High performance speaker identification and verification systems based on Gaussian mixture speaker models: robust, statistically based representations of speaker identity, evaluated on four publically available speech databases.