Best Practices for Speech Corpora in Linguistic Research Workshop ...

• comparative linguistic research of strategies of reading
and speaking the same texts by different speakers.
• research of prosodic realization variability of expressing
syntactic structures and semantic concepts;
• comparative research of the cues for detecting expressive
and emotional speech;
• research of morphonological variability.
Recently we applied this framework for the task of automatic
prosodic modeling of utterance and its prosodic type
identification (Skrelin and Kocharov, 2009). To solve that
task we used all available information in the corpus excluding
the canonical phonetic transcription. We automatically
processed the speech and annotation data thus obtaining
various melodic features. Features essential for our analysis
were added to the original annotation scheme (see figure
2):
• smoothed and interpolated fundamental frequency values
(level 8);
• extreme values of fundamental frequency (level 9);
• boundaries of melodic movements (level 11);
• main melodic movements within the utterance corresponding
to the largest drop, the largest rise, the movement
reaching the global minimum, the movement
reaching the global maximum (level 10).
This year we launched a project on creation the articulatory
data corpus of the Russian speech. The speech data will include
speech signal and articulatory data expressed by both
EMA and video data. The scalable framework allows using
multimedia data as the annotation is following the general
ideas described above. We are able to combine annotation
of different media in one annotation scheme.
4. Conclusion
The comprehensive framework for linguistic research is
presented in the paper. The major features of the framework
are as follows. The annotation is strictly hierarchical,
scalable and allows the assignment of any number of annotation
attributes to segmental units. This makes it possible
to easily extend the speech corpus by the individual
automatically produced annotation. There is a possibility
of complex search and extraction of precise relevant slices
of speech data. The output of processing result is linguistically
sensible and could be individually set up in different
cases.
The speech corpora framework is successfully used for
many various linguistic tasks including those concerning
simultaneous processing of different levels of language.
5. Acknowledgements
The authors acknowledge Saint-Petersburg State University
for a research grant # 31.37.106.2011.
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6. References
Paul Boersma and David Weenink. 2012. Praat: doing
phonetics by computer (version 5.3.04) [computer program].
Liya Bondarko. 2009. Short description of russian sound
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Phonetics of Russian and Finnish. General Introduction.
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Lang GmbH.
N. Grønnum. 2009. A danish phonetically annotated spontaneous
speech corpus (danpass). Speech Communication,
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Pavel Skrelin and Daniil Kocharov. 2009. Avtomaticheskaya
obrabotka prosodichekogo oformleniya viskazivaniya:
relevantnye priznaki dlya avtomaticheskoj
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Pavel Skrelin, Nina Volskaya, Daniil Kocharov, Karina Evgrafova,
Olga Glotova, and Vera Evdokimova. 2010.
Corpres – corpus of russian professionally read speech.
In Proceedings of the 13th International Conference on
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Svetlana Tananayko, Daniil Kocharov, and Ksenia
Sadurtinova. 2011. Programma statisticheskoj
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and Computer, pages 457–462, Kazan, Russia. Moscow
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