Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
MPE-based discriminative linear transforms for speaker adaptation
Computer Speech and Language
Experiments on automatic recognition of nonnative Arabic speech
EURASIP Journal on Audio, Speech, and Music Processing - Scalable Audio-Content Analysis
Speaker adaptation based on MAP estimation of HMM parameters
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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In this paper we are concerned with the problem of the adaptation of non-native speech in a large-vocabulary speech recognition system for Modern Standard Arabic (MSA). A technique to adapt Hidden Markov Models (HMMs) to foreign accents by using Genetic Algorithms (GAs) in unsupervised mode is presented. The implementation requirements of GAs, such as genetic operators and objective function, have been selected to give more reliability to a global linear transformation matrix. The Minimum Phone Error (MPE) criterion is used as an objective function. The West Point Language Data Consortium (LDC) modern standard Arabic database is used throughout our experiments. Results show that significant decrease of word error rate has been achieved by the evolutionary-based approach compared to conventional Maximum Likelihood Linear Regression (MLLR), Maximum a posteriori (MAP) techniques and to the adaptation combining MLLR and MPE-based training.