Impact of Irregular Pronunciation on Phonetic Segmentation of Nijmegen Corpus of Casual?Czech

详细信息    查看全文

• 关键词：spontaneous speech ; casual speech ; pronunciation reduction ; phonetic segmentation ; NCCCz
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8655
• 期：1
• 页码：499-506
• 全文大小：1,163 KB
• 参考文献：1. Kohler, K.J.: Segmental reduction in connected speech in German: Phonological facts and phonetic explanations. In: Hardcastle, W.J., Marchal, A. (eds.) Speech Production and Speech Modelling, pp. 69-2. Kluwer Academic Publishers (1990)
  2. Ernestus, M.: Voice assimilation and segment reduction in Dutch: A corpus-based study of the phonology-phonetics interface. LOT, Utrecht (2000)
  3. Johnson, K.: Massive reduction in conversational American English. In: Yoneyama, K., Maekawa, K. (eds.) Proc. of the 10th International Symposium on Spontaneous Speech: Data and Analysis, Tokyo, Japan, pp. 29-4 (2004)
  4. Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition. Signal Processing Magazine, 82-7 (2012)
  5. Vaněk, J., Psutka, J.V.: Gender-dependent acoustic models fusion developed for automatic subtitling of parliament meetings broadcasted by the Czech TV. In: Sojka, P., Horák, A., Kope?ek, I., Pala, K. (eds.) TSD 2010. LNCS (LNAI), vol.?6231, pp. 431-38. Springer, Heidelberg (2010) CrossRef
  6. Ernestus, M., Ko?ková-Amortová, L., Pollak, P.: The Nijmegen Corpus of Casual Czech. In: LREC 2014, Reykjavik, Iceland, May 26-31 (2014)
  7. Pollak, P., ?ernocky, J.: Czech SPEECON adult database. Technical report (November 2003), http://www.speechdat.org/speecon
  8. Pollak, P., ?ernocky, J., et al.: Speechdat(E) -Eastern European telephone speech databases. In: Proc of XLDB, Athens, Greece (2000)
  9. Siemund, R., H?ge, H., Kunzmann, S., Marasek, K.: SPEECON -Speech data for consumer devices. In: Proc. of the LREC 2000, Athens, Greece (2000)
  10. Hanzl, V., Pollak, P.: Tool for Czech Pronunciation Generation Combining Fixed Rules with Pronunciation Lexicon and Lexicon Management Tool. In: Proc. of LREC 2002, Las Palmas de Gran Canaria, Spain, pp. 1264-269 (2002)
  11. Cmejla, R., et al.: Bayesian changepoint detection for the automatic assessment of fluency and articulatory disorders. Speech Communication, 178-89 (2013)
  12. Mizera, P., Pollak, P.: Accuracy of HMM-based phonetic segmentation using monophone or triphone acoustic model. In: Proc. of Applied Electronics, Pilsen, Czech Republic (2013)
  13. Schwarz, P.: Phoneme recognition based on long temporal context. PhD Thesis, Brno University of Technology (2009)
  14. Povey, D., Ghoshal, A., et al.: The Kaldi Speech Recognition Toolkit. In: Proc. of ASRU, Hawaii, USA (2011)
  15. Pollak, P., Volin, J., Skarnitzl, R.: Phone Segmentation Tool with Integrated Pronunciation Lexicon and Czech Phonetically Labelled Reference Database. In: Proc of LREC, Marrakech, Morocco (2008)
  
• 作者单位：Petr Mizera (21)
  Petr Pollak (21)
  Alice Kolman (22)
  Mirjam Ernestus (23)
  
  21. Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
  22. Radboud University Nijmegen & Christian University of Applied Sciences CHE, Netherlands
  23. Radboud University Nijmegen & Max Planck Institute for Psycholinguistics, Netherlands
  
• ISSN：1611-3349

文摘

This paper describes the pilot study of phonetic segmentation applied to Nijmegen Corpus of Casual Czech (NCCCz). This corpus contains informal speech of strong spontaneous nature which influences the character of produced speech at various levels. This work is the part of wider research related to the analysis of pronunciation reduction in such informal speech. We present the analysis of the accuracy of phonetic segmentation when canonical or reduced pronunciation is used. The achieved accuracy of realized phonetic segmentation provides information about general accuracy of proper acoustic modelling which is supposed to be applied in spontaneous speech recognition. As a byproduct of presented spontaneous speech segmentation, this paper also describes the created lexicon with canonical pronunciations of words in NCCCz, a tool supporting pronunciation check of lexicon items, and finally also a minidatabase of selected utterances from NCCCz manually labelled on phonetic level suitable for evaluation purposes.