Structural Metadata Annotation
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Structural Metadata Annotation of Speech Corpora: Comparing Broadcast News and Broadcast Conversations PowerPoint PPT Presentation

Structural Metadata Annotation of Speech Corpora: Comparing Broadcast News and Broadcast Conversations. J áchym Kolář Jan Švec. University of West Bohemia in Pilsen, Czech Republic. Talk Overview. Structural metadata annotation Speech data Statistics about fillers

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Structural Metadata Annotation of Speech Corpora: Comparing Broadcast News and Broadcast Conversations

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Structural metadata annotation of speech corpora

Structural Metadata Annotation of Speech Corpora:Comparing Broadcast News and Broadcast Conversations

Jáchym KolářJan Švec

University of West Bohemia in Pilsen, Czech Republic


Talk overview

Talk Overview

  • Structural metadata annotation

  • Speech data

  • Statistics about fillers

  • Statistics about edit disfluencies

  • Statistics about sentence-like units

  • Summary

J. Kolar and J. Svec


Structural metadata extraction

Structural Metadata Extraction

  • Metadata Extraction (MDE) research started as part of DARPA EARS program

  • Metadata annotation scheme for MDE introduced by LDC (originally for English  we have extended it to Czech)

  • ULTIMATE GOAL of MDE:

    Automatic conversion of raw speech recognition outputto forms more useful to humansand downstream automatic processes

J. Kolar and J. Svec


Mde annotation subtasks

MDE Annotation Subtasks

  • Boundaries of syntactic/semantic units (SUs)

    • Statements, Interrogatives, Incompletes

    • Coordination breaks, Clausal breaks

  • Non-content words (fillers):

    • Filled pauses (FPs)

    • Discourse markers (DMs)

  • Speech disfluencies (edits):

    • Deletable regions (DelRegs), Interruption points,

      Explicit editing terms, Corrections

J. Kolar and J. Svec


Mde annotation example

MDE Annotation Example

but I you know really [pre-]*uhprefer this form [of]*ofum presentation/.[she]*Sheila told me [on Tuesday]*noon Wednesday/, she didn’t/.so let’s move on/, because we [don’t have]*uhdon’t have time/.well do you like [this]*this example/?

but I you know really pre- uh prefer this form of of um presentation she Sheila told me on Tuesday no on Wednesday she didn’t so let’s move on because we don’t have uh don’t have time well do you like this this example

J. Kolar and J. Svec


Goal of this paper

Goal of This Paper

  • Analyse and compare two Czech MDE corpora from different domains in terms of metadatastatistics

  • Compare Czech Broadcast News (BN) vs. Broadcast Conversations (BC)

  • Also compare Czech and English MDE corpora – English Broadcast News and Conversational Telephone Speech (CTS)

J. Kolar and J. Svec


Czech broadcast news data

Czech Broadcast News Data

  • News from 3 TV channels and 4 radio stations

  • Both public and commercial broadcast companies

  • Differing in presentation style

  • 26 hours of transcribed speech

  • ~ 300 speakers

  • Speech recordings and verbatim transcripts publicly available from LDC

J. Kolar and J. Svec


Broadcast conversation data

Broadcast Conversation Data

  • 52 recordings of a Czech radio talk show – Radioforum

  • 24 hours of transcribed speech

  • ~ 100 speakers

  • 1-3 guests spontaneously answer questions asked by 1-2 interviewers

  • Mostly political debates

  • Currently being extended by additional 20 recordings (~10 hours)

J. Kolar and J. Svec


Statistics about fillers

Statistics about Fillers

  • Filled pauses more frequent in Czech Broadcast Conversations (3.8% of words) than in News (0.5%)

  • English MDE: CTS – 2.2%, BN – 1.4%

  • Discourse markers also more frequent in Czech Conversations (1.6%) than in News (0.1%)

  • English MDE: CTS – 4.4%, BN – 0.5%

J. Kolar and J. Svec


Statistics about edit disfluencies

Statistics about Edit Disfluencies

  • Deletable regions – 2.8% of words in Conversations and 0.2% in News

  • English MDE: 5.4% in CTS and 1.5% in BN

  • Percentage of disfluencies having a correction larger in News (94.6%) than in Conversations (83.8%)

  • Explicit editing terms rare in both corpora –

    occur just at 4% of disfluencies

J. Kolar and J. Svec


Pos analysis of edit disfluencies

POS Analysis of Edit Disfluencies

  • Tagged the Czech corpora employing an automatic POS tagger

  • Czech uses structured tags with 15 positions;

    we only used the first position distinguishing 10 basic POS

  • Computed and compared three POS distributions:

    • Whole corpus

    • Deletable regions only

    • Corrections only

J. Kolar and J. Svec


Pos analysis of edit disfluencies1

POS Analysis of Edit Disfluencies

J. Kolar and J. Svec


Statistics about sus

Statistics about SUs

  • Average SU length: Conversations (14.5 words) shows longer SUs than News (13.0)

  • English BN (12.5) similar to Czech, but CTS shows much shorter SUs (7.0) than Broadcast Conversations

  • SU-internal breaks (clausal and coordination) more frequent in Conversations than in News

    (49% vs. 31% of all SU symbols)

    Complex and compound sentences more common in spontaneous conversations than in prearranged news

J. Kolar and J. Svec


Summary

Summary

  • Broadcast Conversations contain significantly more fillers and disfluencies than News

  • Conversations also show longer SUs and contain a higher number of complex sentences than News

  • Deletable regions and corrections in both corpora show different POS distributions in comparison with the general POS distributions

  • We plan to make Czech MDE corpora publicly available

J. Kolar and J. Svec


Structural metadata annotation of speech corpora

Structural Metadata Annotation of Speech Corpora:Comparing Broadcast News and Broadcast Conversations

Jáchym KolářJan Švec

University of West Bohemia in Pilsen, Czech Republic


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