Prosodic boundary detection using syntactic and acoustic information

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Abstract

This paper presents a two-stage procedure for automatic prosodic boundary detection in Russian based on textual and acoustic data. The key idea of the method is (1) to predict all potential prosodic boundaries based on syntax and (2) among these potential boundaries, to choose those which are marked acoustically. For the first stage we developed a system which predicted a potential boundary whenever two adjacent words were not connected with each other in terms of syntax; for this we used a dependency tree parser and added several simple rules. At the second stage we run a random forest classifier to detect the actual prosodic boundaries using a small set of acoustic features. Of all the observed prosodic features pause duration worked best, and for some speakers it could be used as the only acoustic cue with no change in efficiency. For other speakers, however, other features were useful, such as tempo and amplitude resets or F 0 range, and the choice of the features was speaker-dependent. In the end the procedure worked with the F 1 measure of 0.91, recall of 0.90 and precision of 0.93, which is the best published result for Russian.

LanguageEnglish
Pages231-241
Number of pages11
JournalComputer Speech and Language
Volume53
DOIs
StatePublished - 1 Jan 2019

Keywords

  • Acoustic feature
  • Automatic boundary detection
  • Dependency parsing
  • Prosodic phrasing
  • Russian

Scopus subject areas

  • Arts and Humanities(all)
  • Software
  • Theoretical Computer Science
  • Human-Computer Interaction

Cite this

@article{110e529bebe84ba4bab532c650e4e32d,
title = "Prosodic boundary detection using syntactic and acoustic information",
abstract = "This paper presents a two-stage procedure for automatic prosodic boundary detection in Russian based on textual and acoustic data. The key idea of the method is (1) to predict all potential prosodic boundaries based on syntax and (2) among these potential boundaries, to choose those which are marked acoustically. For the first stage we developed a system which predicted a potential boundary whenever two adjacent words were not connected with each other in terms of syntax; for this we used a dependency tree parser and added several simple rules. At the second stage we run a random forest classifier to detect the actual prosodic boundaries using a small set of acoustic features. Of all the observed prosodic features pause duration worked best, and for some speakers it could be used as the only acoustic cue with no change in efficiency. For other speakers, however, other features were useful, such as tempo and amplitude resets or F 0 range, and the choice of the features was speaker-dependent. In the end the procedure worked with the F 1 measure of 0.91, recall of 0.90 and precision of 0.93, which is the best published result for Russian.",
keywords = "Prosodic phrasing, Automatic boundary detection, Dependency parsing, Acoustic feature, Russian, Acoustic feature, Automatic boundary detection, Dependency parsing, Prosodic phrasing, Russian",
author = "Кочаров, {Даниил Александрович} and Качковская, {Татьяна Васильевна} and Скрелин, {Павел Анатольевич}",
year = "2019",
month = "1",
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doi = "10.1016/j.csl.2018.07.001",
language = "English",
volume = "53",
pages = "231--241",
journal = "Computer Speech and Language",
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AU - Кочаров, Даниил Александрович

AU - Качковская, Татьяна Васильевна

AU - Скрелин, Павел Анатольевич

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This paper presents a two-stage procedure for automatic prosodic boundary detection in Russian based on textual and acoustic data. The key idea of the method is (1) to predict all potential prosodic boundaries based on syntax and (2) among these potential boundaries, to choose those which are marked acoustically. For the first stage we developed a system which predicted a potential boundary whenever two adjacent words were not connected with each other in terms of syntax; for this we used a dependency tree parser and added several simple rules. At the second stage we run a random forest classifier to detect the actual prosodic boundaries using a small set of acoustic features. Of all the observed prosodic features pause duration worked best, and for some speakers it could be used as the only acoustic cue with no change in efficiency. For other speakers, however, other features were useful, such as tempo and amplitude resets or F 0 range, and the choice of the features was speaker-dependent. In the end the procedure worked with the F 1 measure of 0.91, recall of 0.90 and precision of 0.93, which is the best published result for Russian.

AB - This paper presents a two-stage procedure for automatic prosodic boundary detection in Russian based on textual and acoustic data. The key idea of the method is (1) to predict all potential prosodic boundaries based on syntax and (2) among these potential boundaries, to choose those which are marked acoustically. For the first stage we developed a system which predicted a potential boundary whenever two adjacent words were not connected with each other in terms of syntax; for this we used a dependency tree parser and added several simple rules. At the second stage we run a random forest classifier to detect the actual prosodic boundaries using a small set of acoustic features. Of all the observed prosodic features pause duration worked best, and for some speakers it could be used as the only acoustic cue with no change in efficiency. For other speakers, however, other features were useful, such as tempo and amplitude resets or F 0 range, and the choice of the features was speaker-dependent. In the end the procedure worked with the F 1 measure of 0.91, recall of 0.90 and precision of 0.93, which is the best published result for Russian.

KW - Prosodic phrasing

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KW - Dependency parsing

KW - Acoustic feature

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KW - Acoustic feature

KW - Automatic boundary detection

KW - Dependency parsing

KW - Prosodic phrasing

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