Improvement of utterance clustering by using employees' sound and area data

Tetsuya Kawase, Masanori Takehara, Satoshi Tamura, Satoru Hayamizu, Ryuhei Tenmoku, Takeshi Kurata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose to use staying area data toward the estimation of serving time for customers. To classify utterances enables us to estimate conversation types between speakers. However, its performance becomes lower in real environments. We propose a method using area data with sound data to solve this problem. We also propose a method to estimate the conversation types using the decision trees. They were tested with the data recorded in a Japanese restaurant. In the experiment to classify utterances, the proposed method performed better than the method using only sound data. In the experiment to estimate the conversation types, we succeeded to recover 70% of the mis-classified conversations using both of sound and area data.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3047-3051
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 2014 May 42014 May 9

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period14/5/414/5/9

Keywords

  • Area data
  • Conversation type
  • Serving time for customers
  • Utterance classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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