Automated Development of DNN Based Spoken Language Systems Using Evolutionary Algorithms

Takahiro Shinozaki, Shinji Watanabe, Kevin Duh

研究成果: Chapter

抄録

Spoken language processing is one of the research areas that has contributed significantly to the recent revival in neural network research. For example, speech recognition has been at the forefront of deep learning research, inventing various novel models. Their dramatic performance improvements compared to previous state-of-the-art implementations have resulted in spoken language systems being deployed in a wide range of applications today. However, these systems require intensive tuning of their network designs and the training setups in order to achieve maximal performance. The laborious effort by human experts is becoming a prominent obstacle in system development. In this chapter, we first explain the basic concepts and the neural network-based implementations of spoken language processing systems. Several types of neural network models will be described. We then introduce our effort to automate the tuning of the system meta-parameters using evolutionary algorithms.

本文言語English
ホスト出版物のタイトルNatural Computing Series
出版社Springer
ページ97-129
ページ数33
DOI
出版ステータスPublished - 2020
外部発表はい

出版物シリーズ

名前Natural Computing Series
ISSN(印刷版)1619-7127

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science

フィンガープリント 「Automated Development of DNN Based Spoken Language Systems Using Evolutionary Algorithms」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル