Spoken Language Acquisition Based on Reinforcement Learning and Word Unit Segmentation

Shengzhou Gao; Wenxin Hou; Tomohiro Tanaka; Takahiro Shinozaki

ICASSP 2020accepted0 citations

Spoken Language Acquisition Based on Reinforcement Learning and Word Unit Segmentation

Shengzhou Gao, Wenxin Hou, Tomohiro Tanaka, Takahiro Shinozaki

Abstract

The process of spoken-language acquisition has been one of the topics of greatest interest to linguists for decades. By uti-lizing modern machine learning techniques, we simulated this process on computers, which helps to understand it and develop new possibilities of applying this concept on intelligent robots, among other things. This paper proposes a new framework for simulating spoken-language acquisition by combining reinforcement learning and unsupervised learning methods. Our experiments also show that a spoken language can be acquired considerably faster by identifying potential word segments from collected ambient sounds in an unsupervised manner.

BibTeX

@inproceedings{icassp2020_spokenlanguageac,
  title = {Spoken Language Acquisition Based on Reinforcement Learning and Word Unit Segmentation},
  author = {Shengzhou Gao and Wenxin Hou and Tomohiro Tanaka and Takahiro Shinozaki},
  booktitle = {ICASSP 2020},
  year = {2020}
}