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

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• 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. 46 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 system. In Viola de Silva and Riikka Ullakonoja, editors, Phonetics of Russian and Finnish. General Introduction. Spontaneous and Read-aloud Speech, pages 23–37. Peter Lang GmbH. N. Grønnum. 2009. A danish phonetically annotated spontaneous speech corpus (danpass). Speech Communication, 51:594–603. Pavel Skrelin and Daniil Kocharov. 2009. Avtomaticheskaya obrabotka prosodichekogo oformleniya viskazivaniya: relevantnye priznaki dlya avtomaticheskoj interpretatsii intonatsionnoj modeli. In Trudy tretiego mezhdistsiplinarnogo seminara Analiz razgovornoj russkoj rechi (AR3-2009), pages 41–46, Saint- Petersburg. 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 Text, Speech and Dialogue, pages 386–393, Brno, Czech Republic. Springer Verlag. Svetlana Tananayko, Daniil Kocharov, and Ksenia Sadurtinova. 2011. Programma statisticheskoj obrabobtki korpusa rechevih dannih. In Proceedings of the 14th International Conference on Speech and Computer, pages 457–462, Kazan, Russia. Moscow State Linguistic University.
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Page 1 and 2: Best Practices for Speech Corpora i
Page 3 and 4: Editors Michael Haugh Griffith Univ
Page 5 and 6: Author Index Broeder, Daan ........
Page 7 and 8: A linguistics-based speech corpus J
Page 9 and 10: Figure 2: Grammatical tags are visi
Page 11: In Jokinen, Kristiina and Eckhard B
Page 14 and 15: 2.2 Parameters of the Corpus Design
Page 16 and 17: switching and code mixing, we have
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Page 20 and 21: French and Russian screen versions
Page 22 and 23: singularity, expressiveness, semant
Page 24 and 25: is often fluid in terms of communic
Page 26 and 27: annotation. Researchers have often
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Page 30 and 31: in various research and/or student
Page 32 and 33: As previously mentioned, the degree
Page 34 and 35: 9. References Corpus of Spoken Gree
Page 36 and 37: In addition to part-of-speech tags
Page 38 and 39: POS description example translitera
Page 40 and 41: Figure 4: Different syntactic analy
Page 42 and 43: Herbert H. Clark and Thomas Wasow.
Page 44 and 45: The ‘externality’ of DA arises
Page 46 and 47: 6. Data analysis Each turn is annot
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