Detail publikace

Manual and Semi-Automatic Approaches to Building a Multilingual Phoneme Set

Originální název

Manual and Semi-Automatic Approaches to Building a Multilingual Phoneme Set

Anglický název

Manual and Semi-Automatic Approaches to Building a Multilingual Phoneme Set

Jazyk

en

Originální abstrakt

This articles describes a comparison between manual and semi-automatic approaches to building a multilingual phoneme set. The two approaches were compared in cases of 1) a multilingual system with abundant data for all the languages, 2) multilingual systems excluding target language 3) multilingual systems with small amount of data for target languages. The work shows that careful choice of merging methods can help improve recognition of languages with no or little training data and reasonably reduce multilingual phoneme set without losing a lot of accuracy.

Anglický abstrakt

This articles describes a comparison between manual and semi-automatic approaches to building a multilingual phoneme set. The two approaches were compared in cases of 1) a multilingual system with abundant data for all the languages, 2) multilingual systems excluding target language 3) multilingual systems with small amount of data for target languages. The work shows that careful choice of merging methods can help improve recognition of languages with no or little training data and reasonably reduce multilingual phoneme set without losing a lot of accuracy.

BibTex


@inproceedings{BUT103490,
  author="Ekaterina {Egorova} and Karel {Veselý} and Martin {Karafiát} and Miloš {Janda} and Jan {Černocký}",
  title="Manual and Semi-Automatic Approaches to Building a Multilingual Phoneme Set",
  annote="This articles describes a comparison between manual and semi-automatic approaches
to building a multilingual phoneme set. The two approaches were compared in cases
of 1) a multilingual system with abundant data for all the languages, 2)
multilingual systems excluding target language 3) multilingual systems with small
amount of data for target languages. The work shows that careful choice of
merging methods can help improve recognition of languages with no or little
training data and reasonably reduce multilingual phoneme set without losing a lot
of accuracy.",
  address="IEEE Signal Processing Society",
  booktitle="Proceedings of ICASSP 2013",
  chapter="103490",
  edition="NEUVEDEN",
  howpublished="print",
  institution="IEEE Signal Processing Society",
  year="2013",
  month="may",
  pages="7324--7328",
  publisher="IEEE Signal Processing Society",
  type="conference paper"
}