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Description
  • Most modern speech synthesis systems utilize large speech corpora to learn new voices. These speech corpora usually contain several hours of speech spoken by talented speakers who are able to record such an amount of speech data in a sufficient quality. An appropriate phonetic and prosodic annotation of the recorded utterances is necessary for a high quality of synthesized speech. For many languages, the pitch shape within the last prosodic word of a phrase is characteristic for particular types of sentences and phrase structure of compound/complex sentences. However in the real data, this formal convention can be breached and a different pitch shape than expected can be present. This can be a source of prosody inconsistency in synthesized speech. This article presents some experiments on automatic detection of prosodic mismatch in recorded utterances. A simple classifier based on GMM was proposed for this task. Experiments were performed on 5 large speech corpora. The classification results were successfully verified by listening tests.
  • Most modern speech synthesis systems utilize large speech corpora to learn new voices. These speech corpora usually contain several hours of speech spoken by talented speakers who are able to record such an amount of speech data in a sufficient quality. An appropriate phonetic and prosodic annotation of the recorded utterances is necessary for a high quality of synthesized speech. For many languages, the pitch shape within the last prosodic word of a phrase is characteristic for particular types of sentences and phrase structure of compound/complex sentences. However in the real data, this formal convention can be breached and a different pitch shape than expected can be present. This can be a source of prosody inconsistency in synthesized speech. This article presents some experiments on automatic detection of prosodic mismatch in recorded utterances. A simple classifier based on GMM was proposed for this task. Experiments were performed on 5 large speech corpora. The classification results were successfully verified by listening tests. (en)
Title
  • Initial Experiments on Automatic Correction of Prosodic Annotation of Large Speech Corpora
  • Initial Experiments on Automatic Correction of Prosodic Annotation of Large Speech Corpora (en)
skos:prefLabel
  • Initial Experiments on Automatic Correction of Prosodic Annotation of Large Speech Corpora
  • Initial Experiments on Automatic Correction of Prosodic Annotation of Large Speech Corpora (en)
skos:notation
  • RIV/49777513:23520/14:43922838!RIV15-TA0-23520___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0090), P(TA01011264)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
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  • 21866
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/14:43922838
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • prosodeme; prosodic annotation; speech corpora (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [9E8475D11736]
http://linked.open...v/mistoKonaniAkce
  • Brno
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Text, Speech, and Dialogue
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Grůber, Martin
  • Hanzlíček, Zdeněk
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-319-10816-2_58
http://purl.org/ne...btex#hasPublisher
  • Springer International Publishing
https://schema.org/isbn
  • 978-3-319-10815-5
http://localhost/t...ganizacniJednotka
  • 23520
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