Machine learning techniques for the identification of cues for stop place

Madelaine C. Plauché, Mustafa (Kemal) Sonmez

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

2 Scopus citations

Abstract

This paper is situated in a long line of phonetic studies that seek to determine and qualify the acoustic cues humans use to identify stop place. The present study draws from a database of 1500 CV tokens of American English and their values for the acoustic features thought to be cues for stop place identification, including (1) VOT, (2) energy of the burst and release, (3) spectrum at the burst, and (4) formant transitions into the following vowel. Decision trees are used to determine the relative invariance of these acoustic features, which indicates their potential to serve as useful cues for listeners cross-contextually. Decision trees thus allow the evaluation of vocalic effects on this hierarchy of features for the purpose of guiding classic perceptual confusion studies.

Original languageEnglish (US)
Title of host publication6th International Conference on Spoken Language Processing, ICSLP 2000
PublisherInternational Speech Communication Association
ISBN (Electronic)7801501144, 9787801501141
StatePublished - 2000
Externally publishedYes
Event6th International Conference on Spoken Language Processing, ICSLP 2000 - Beijing, China
Duration: Oct 16 2000Oct 20 2000

Other

Other6th International Conference on Spoken Language Processing, ICSLP 2000
CountryChina
CityBeijing
Period10/16/0010/20/00

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics

Fingerprint Dive into the research topics of 'Machine learning techniques for the identification of cues for stop place'. Together they form a unique fingerprint.

  • Cite this

    Plauché, M. C., & Sonmez, M. K. (2000). Machine learning techniques for the identification of cues for stop place. In 6th International Conference on Spoken Language Processing, ICSLP 2000 International Speech Communication Association.