Adaptation to non-native speech using evolutionary-based discriminative linear transforms

作者：Selouani Sid Ahmed^*; Alotaibi Yousef Ajami

来源：Engineering Applications of Artificial Intelligence, 2013, 26(2): 899-904.

DOI：10.1016/j.engappai.2012.06.006

• Speaker adaptation
• Discriminative training
• Non-native speech
• Genetic algorithms
• Hidden Markov models
• Maximum likelihood linear regression

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.

• 出版日期2013-2