Ensemble learning approaches in speech recognition

Abstract

An overview is made on the ensemble learning efforts that have emerged in automatic speech recognition in recent years. The approaches that are based on different machine learning techniques and target various levels and components of speech recognition are described, and their effectiveness is discussed in terms of the direct performance measure of word error rate and the indirect measures of classification margin, diversity, as well as bias and variance. In addition, methods on reducing storage and computation costs of ensemble models for practical deployments of speech recognition systems are discussed. Ensemble learning for speech recognition has been largely fruitful, and it is expected to continue progress along with the advances in machine learning, speech and language modeling, as well as computing technology.