Multi-class Extractive Voicemail Summarization

Konstantinos Koumpis; Steve Renals

Multi-class Extractive Voicemail Summarization

Konstantinos Koumpis, Steve Renals

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper is about a system that extracts principal content words from speech-recognized transcripts of voicemail messages and classifies them into proper names, telephone numbers, dates/times and `other'. The short text summaries generated are suitable for mobile messaging applications. The system uses a set of classifiers to identify the summary words, with each word being identified by a vector of lexical and prosodic features. The features are selected using Parcel, an ROC-based algorithm. We visually compare the role of a large number of individual features and discuss effective ways to combine them. We finally evaluate their performance on manual and automatic transcriptions derived from two different speech recognition systems.

Original language	English
Title of host publication	Proceedings of the 8th European Conference on Speech Communication and Technology
Subtitle of host publication	Eurospeech 2003 - Interspeech 2003
Publisher	ISCA
Pages	2785-2788
Number of pages	4
Publication status	Published - 2003
Event	8th European Conference on Speech Communication and Technology (Eurospeech 2003) - Geneva, Switzerland Duration: 1 Sept 2003 → 4 Sept 2003

Conference

Conference	8th European Conference on Speech Communication and Technology (Eurospeech 2003)
Country/Territory	Switzerland
City	Geneva
Period	1/09/03 → 4/09/03

Access to Document

http://www.isca-speech.org/archive/eurospeech_2003/e03_2785.html

Cite this

@inproceedings{fa9cfacb6f96465993782563b43b4c58,

title = "Multi-class Extractive Voicemail Summarization",

abstract = "This paper is about a system that extracts principal content words from speech-recognized transcripts of voicemail messages and classifies them into proper names, telephone numbers, dates/times and `other'. The short text summaries generated are suitable for mobile messaging applications. The system uses a set of classifiers to identify the summary words, with each word being identified by a vector of lexical and prosodic features. The features are selected using Parcel, an ROC-based algorithm. We visually compare the role of a large number of individual features and discuss effective ways to combine them. We finally evaluate their performance on manual and automatic transcriptions derived from two different speech recognition systems.",

author = "Konstantinos Koumpis and Steve Renals",

year = "2003",

language = "English",

pages = "2785--2788",

booktitle = "Proceedings of the 8th European Conference on Speech Communication and Technology",

publisher = "ISCA",

note = "8th European Conference on Speech Communication and Technology (Eurospeech 2003) ; Conference date: 01-09-2003 Through 04-09-2003",

}

Koumpis, K & Renals, S 2003, Multi-class Extractive Voicemail Summarization. in Proceedings of the 8th European Conference on Speech Communication and Technology: Eurospeech 2003 - Interspeech 2003. ISCA, pp. 2785-2788, 8th European Conference on Speech Communication and Technology (Eurospeech 2003), Geneva, Switzerland, 1/09/03. <http://www.isca-speech.org/archive/eurospeech_2003/e03_2785.html>

TY - GEN

T1 - Multi-class Extractive Voicemail Summarization

AU - Koumpis, Konstantinos

AU - Renals, Steve

PY - 2003

Y1 - 2003

N2 - This paper is about a system that extracts principal content words from speech-recognized transcripts of voicemail messages and classifies them into proper names, telephone numbers, dates/times and `other'. The short text summaries generated are suitable for mobile messaging applications. The system uses a set of classifiers to identify the summary words, with each word being identified by a vector of lexical and prosodic features. The features are selected using Parcel, an ROC-based algorithm. We visually compare the role of a large number of individual features and discuss effective ways to combine them. We finally evaluate their performance on manual and automatic transcriptions derived from two different speech recognition systems.

AB - This paper is about a system that extracts principal content words from speech-recognized transcripts of voicemail messages and classifies them into proper names, telephone numbers, dates/times and `other'. The short text summaries generated are suitable for mobile messaging applications. The system uses a set of classifiers to identify the summary words, with each word being identified by a vector of lexical and prosodic features. The features are selected using Parcel, an ROC-based algorithm. We visually compare the role of a large number of individual features and discuss effective ways to combine them. We finally evaluate their performance on manual and automatic transcriptions derived from two different speech recognition systems.

M3 - Conference contribution

SP - 2785

EP - 2788

BT - Proceedings of the 8th European Conference on Speech Communication and Technology

PB - ISCA

T2 - 8th European Conference on Speech Communication and Technology (Eurospeech 2003)

Y2 - 1 September 2003 through 4 September 2003

ER -

Multi-class Extractive Voicemail Summarization

Abstract

Conference

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Cite this