Analysing the prosodic characteristics of speech-chunks preceding silences in task-based interactions

John Kane; Irena Yanushevskaya; Céline De Looze; Brian Vaughan; Ailbhe Ní Chasaide

Analysing the prosodic characteristics of speech-chunks preceding silences in task-based interactions

John Kane, Irena Yanushevskaya, Céline De Looze, Brian Vaughan, Ailbhe Ní Chasaide

School of Media

Research output: Contribution to journal › Conference article › peer-review

Abstract

For many applications in human-computer interaction, it is desirable to predict between-(gaps) and within-(pauses) speaker silences independently of automatic speech recognition (ASR). In this study, we focus a dataset of 6 dyadic task-based interactions and aim at automatic discrimination of gaps and pauses based on f0, energy and glottal parameters derived from the speech just preceding the silence. Initial manual annotation reveals strong discriminative power of intonation tune types. In a subsequent automatic analysis using descriptive statistics of parameter contours, as well as a modelling of such contours using principal component analysis, we are able to speakerindependently predict pauses and gaps at an accuracy of 70 % compared to a 56 % baseline.

Original language	English
Pages (from-to)	333-337
Number of pages	5
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication status	Published - 2014
Event	15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, Singapore Duration: 14 Sep 2014 → 18 Sep 2014

Keywords

Glottal source
Prosody
Speech timing
Spoken interaction
Turn-taking

Cite this

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title = "Analysing the prosodic characteristics of speech-chunks preceding silences in task-based interactions",

abstract = "For many applications in human-computer interaction, it is desirable to predict between-(gaps) and within-(pauses) speaker silences independently of automatic speech recognition (ASR). In this study, we focus a dataset of 6 dyadic task-based interactions and aim at automatic discrimination of gaps and pauses based on f0, energy and glottal parameters derived from the speech just preceding the silence. Initial manual annotation reveals strong discriminative power of intonation tune types. In a subsequent automatic analysis using descriptive statistics of parameter contours, as well as a modelling of such contours using principal component analysis, we are able to speakerindependently predict pauses and gaps at an accuracy of 70 \% compared to a 56 \% baseline.",

keywords = "Glottal source, Prosody, Speech timing, Spoken interaction, Turn-taking",

author = "John Kane and Irena Yanushevskaya and \{De Looze\}, C{\'e}line and Brian Vaughan and \{N{\'i} Chasaide\}, Ailbhe",

note = "Publisher Copyright: Copyright {\textcopyright} 2014 ISCA.; 15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 ; Conference date: 14-09-2014 Through 18-09-2014",

year = "2014",

language = "English",

pages = "333--337",

journal = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",

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publisher = "International Speech Communication Association",

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TY - JOUR

T1 - Analysing the prosodic characteristics of speech-chunks preceding silences in task-based interactions

AU - Kane, John

AU - Yanushevskaya, Irena

AU - De Looze, Céline

AU - Vaughan, Brian

AU - Ní Chasaide, Ailbhe

PY - 2014

Y1 - 2014

N2 - For many applications in human-computer interaction, it is desirable to predict between-(gaps) and within-(pauses) speaker silences independently of automatic speech recognition (ASR). In this study, we focus a dataset of 6 dyadic task-based interactions and aim at automatic discrimination of gaps and pauses based on f0, energy and glottal parameters derived from the speech just preceding the silence. Initial manual annotation reveals strong discriminative power of intonation tune types. In a subsequent automatic analysis using descriptive statistics of parameter contours, as well as a modelling of such contours using principal component analysis, we are able to speakerindependently predict pauses and gaps at an accuracy of 70 % compared to a 56 % baseline.

AB - For many applications in human-computer interaction, it is desirable to predict between-(gaps) and within-(pauses) speaker silences independently of automatic speech recognition (ASR). In this study, we focus a dataset of 6 dyadic task-based interactions and aim at automatic discrimination of gaps and pauses based on f0, energy and glottal parameters derived from the speech just preceding the silence. Initial manual annotation reveals strong discriminative power of intonation tune types. In a subsequent automatic analysis using descriptive statistics of parameter contours, as well as a modelling of such contours using principal component analysis, we are able to speakerindependently predict pauses and gaps at an accuracy of 70 % compared to a 56 % baseline.

KW - Glottal source

KW - Prosody

KW - Speech timing

KW - Spoken interaction

KW - Turn-taking

UR - https://www.scopus.com/pages/publications/84910067014

M3 - Conference article

AN - SCOPUS:84910067014

SN - 2308-457X

SP - 333

EP - 337

JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

T2 - 15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014

Y2 - 14 September 2014 through 18 September 2014

ER -

Analysing the prosodic characteristics of speech-chunks preceding silences in task-based interactions

Abstract

Keywords

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