Automated Development of DNN Based Spoken Language Systems Using Evolutionary Algorithms

Takahiro Shinozaki*, Shinji Watanabe, Kevin Duh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationNatural Computing Series
Number of pages33
Publication statusPublished - 2020
Externally publishedYes

Publication series

NameNatural Computing Series
ISSN (Print)1619-7127

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science


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