Automatic measurement of pre-aspiration

Yaniv Sheena, Míša Hejná, Yossi Adi, Joseph Keshet

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Pre-aspiration is defined as the period of glottal friction occurring in sequences of vocalic/consonantal sonorants and phonetically voiceless obstruents. We propose two machine learning methods for automatic measurement of pre-aspiration duration: a feedforward neural network, which works at the frame level; and a structured prediction model, which relies on manually designed feature functions, and works at the segment level. The input for both algorithms is a speech signal of an arbitrary length containing a single obstruent, and the output is a pair of times which constitutes the pre-aspiration boundaries. We train both models on a set of manually annotated examples. Results suggest that the structured model is superior to the frame-based model as it yields higher accuracy in predicting the boundaries and generalizes to new speakers and new languages. Finally, we demonstrate the applicability of our structured prediction algorithm by replicating linguistic analysis of pre-aspiration in Aberystwyth English with high correlation.

Original languageAmerican English
Pages (from-to)1049-1053
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2017-August
DOIs
StatePublished - 2017
Externally publishedYes
Event18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017

Bibliographical note

Publisher Copyright:
Copyright © 2017 ISCA.

Keywords

  • Feedforward neural network
  • Laboratory phonology
  • Pre-aspiration
  • Structured prediction

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