A Revised Comparison of Polish Taggers in the Application for Automatic Speech Recognition

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• 关键词：Morphosyntactic tagger ; Polish ; Automatic speech recognition ; Language model
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9561
• 期：1
• 页码：68-81
• 全文大小：249 KB
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  13.Pohl, A., Ziółko, B.: A comparison of polish taggers in the application for automatic speech recognition. In: Proceedings of the 6th Language & Technology Conference, pp. 294–298 (2013)
  14.Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlíček, P., Qian, Y., Schwarz, P., et al.: The kaldi speech recognition toolkit. In: Proceedings of Automatic Speech Recognition and Understanding (2011)
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• 作者单位：Aleksander Smywiński-Pohl (16) (17) (18)
  Bartosz Ziółko (17) (18)
  
  16. Faculty of Management and Social Communication, Jagiellonian University, Kraków, Poland
  17. Faculty of Computer Science, Electronics and Telecommunication, AGH University of Science and Technology, Kraków, Poland
  18. Techmo, Kraków, Poland
  
• 丛书名：Human Language Technology. Challenges for Computer Science and Linguistics
• ISBN：978-3-319-43808-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9561

文摘

In this paper (This is a revised and extended version of the article A Comparison of Polish Taggers in the Application for Automatic Speech Recognition that appeared in the Proceedings of Language and Tools Conference, Poznan, 2013.) we investigate the performance of Polish taggers in the context of automatic speech recognition (ASR). We use a morphosyntactic language model to improve speech recognition in an ASR system and seek the best Polish tagger for our needs. Polish is an inflectional language and an n-gram model using morphosyntactic features, which reduces data sparsity seems to be a good choice. We investigate the difference between the morphosyntactic taggers in that context. We compare the results of tagging with respect to the reduction of word error rate as well as speed of tagging. As it turns out at present the taggers using conditional random fields (CRF) models perform the best in the context of ASR. A broader audience might be also interested in the other discussed features of the taggers such as easiness of installation and usage, which are usually not covered in the papers describing such systems.