A Canonicalization of Distinctive Phonetic Features to Improve Arabic Speech Recognition

2019 ◽  
Vol 105 (6) ◽  
pp. 1269-1277 ◽  
Author(s):  
Yousef A. Alotaibi ◽  
Sid-Ahmed Selouani ◽  
Mohammed Sidi Yakoub ◽  
Yasser Mohammed Seddiq ◽  
Ali Meftah
Keyword(s):  
Markov Models ◽  
Language Model ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Model Systems ◽  
Phoneme Recognition ◽  
Phonetic Features ◽  
Language Context

The robustness of speech classification and recognition systems can be improved by the adoption of language distinctive phonetic feature (DPF) elements that can increase the effective characterization of a speech signal. This paper presents the results of applying Hidden Markov Models (HMMs) that perform Arabic phoneme recognition in conjunction with the inclusion and classification of their DPF element classes. The research focuses on classifying Modern Standard Arabic (MSA) phonemes within isolated words without a language context. HMM-based phoneme recognition is tested using 8, 16, and 32 HMM Gaussian mixture models. The monophone configuration is designed with consideration of 2-gram language model to evaluate the inherent performance of the system. The overall correct rates for classifying DPF element classes for the three versions of HMM systems are 83.29% 88.96%, and 92.70% for 8, 16, and 32 HMM Gaussian mixture model systems, respectively.

scholarly journals On the characterization of flowering curves using Gaussian mixture models

2016 ◽  
Vol 402 ◽  
pp. 75-88 ◽  
Author(s):  
Frédéric Proïa ◽  
Alix Pernet ◽  
Tatiana Thouroude ◽  
Gilles Michel ◽  
Jérémy Clotault
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture

Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models

2006 ◽  
Vol 27 (10) ◽  
pp. 935-951 ◽  
Author(s):  
Felicity R Allen ◽  
Eliathamby Ambikairajah ◽  
Nigel H Lovell ◽  
Branko G Celler
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture

Effects of Syllable Language Model on Distinctive Phonetic Features (DPFs) based Phoneme Recognition Performance

2010 ◽  
Vol 5 (6) ◽  
Author(s):  
Mohammad Nurul Huda ◽  
Manoj Banik ◽  
Ghulam Muhammad ◽  
Mashud Kabir ◽  
Bernd J. Kröger
Keyword(s):  
Recognition Performance ◽  
Language Model ◽  
Phoneme Recognition ◽  
Phonetic Features

scholarly journals New Families in our Solar Neighborhood: Applying Gaussian Mixture Models for Objective Classification of Structures in the Milky Way and in Simulations

2021 ◽  
Vol 921 (2) ◽  
pp. 106
Author(s):  
Farnik Nikakhtar ◽  
Robyn E. Sanderson ◽  
Andrew Wetzel ◽  
Sarah Loebman ◽  
Sanjib Sharma ◽  
...  
Keyword(s):  
Mixture Models ◽  
Milky Way ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Solar Neighborhood ◽  
Objective Classification

scholarly journals Language Identification based on Support Vector Machine using GMM Super vectors

2020 ◽  
Vol 9 (4) ◽  
pp. 1983-1986
Keyword(s):  
Language Model ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Language Identification ◽  
Training Data ◽  
Background Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Map Adaptation ◽  
Universal Background Model

This paper proposes a novel approach that combines the power of generative Gaussian mixture models (GMM) and discriminative support vector machines (SVM). The main objective this paper is to incorporating the GMM super vectors based on SVM classifier for language identification (LID) task. The GMM based LID system to capture all the variations present in phonotactic constraints imposed by the language requires large amount of training data. The Gaussian mixture model (GMM)- universal background model (UBM) modeling require less amount of training data. In GMM-UBM LID system, a language model is created by maximum a posterior (MAP) adaptation of the means of the universal background model (UBM). Here the GMM super vectors are created by concatenating the means of the adapted mixture components from UBM. Then these super vectors are applied to a SVM for classification purpose. In this paper, the performance of GMM-UBM LID system based on SVM is compared with the conventional GMM LID system. Form the performance analysis it is found that GMM-UBM LID system based on SVM is performed well when compared to GMM based LID system.

Sign in / Sign up

Export Citation Format

Share Document