Chapter 2 - End-to-End Acoustic Modeling Using Convolutional Neural Networks

Abstract

State-of-the-art automatic speech recognition (ASR) systems map speech into its corresponding text. Conventional ASR systems model the speech signal into phones in two steps; feature extraction and classifier training. Traditional ASR systems have been replaced by deep neural network (DNN)-based systems. Today, end-to-end ASRs are gaining in popularity due to simplified model-building processes and the ability to directly map speech into text without predefined alignments. These models are based on data-driven learning methods and competition with complicated ASR models based on DNN and linguistic resources. There are three major types of end-to-end architectures for ASR: Attention-based methods, connectionist temporal classification, and convolutional neural network (CNN)-based direct raw speech model.