Abstract
Automatic recognition of an electrolaryngeal speech is usually a hard task due to the fact that all phonemes tend to be voiced. However, using a strong language model (LM) for continuous speech recognition task, we can achieve satisfactory recognition accuracy. On the other hand, the recognition of isolated words or phrase sentences containing only several words poses a problem, as in this case, the LM does not have a chance to properly support the recognition. At the same time, the recognition of short phrases has a great practical potential. In this paper, we would like to discuss poor performance of the electrolaryngeal speech automatic speech recognition (ASR), especially for isolated words. By comparing the results achieved by humans and the ASR system, we will attempt to show that even humans are unable to distinguish the identity of the word, differing only in voicing, always correctly. We describe three experiments: the one represents blind recognition, i.e., the ability to correctly recognize an isolated word selected from a vocabulary of more than a million words. The second experiment shows results achieved when there is some additional knowledge about the task, specifically, when the recognition vocabulary is reduced only to words that actually are included in the test. And the third test evaluates the ability to distinguish two similar words (differing only in voicing) for both the human and the ASR system.
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Notes
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Scythe.
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Goat.
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Zerogram LM is the setting where all words from a fixed vocabulary have the same probability.
References
Brown, D.H., Hilgers, F.J., Irish, J.C., Balm, A.J.: Postlaryngectomy voice rehabilitation: state of the art at the Millennium. World J. Surg. 27(7), 824–831 (2003)
Fuchs, A.K., Morales-Cordovilla, J.A., Hagmller, M.: ASR for electro-laryngeal speech. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 234–238, December 2013
Github Kaldi: https://github.com/kaldi-asr/kaldi/tree/master/egs/wsj/s5
Jůzová, M., Romportl, J., Tihelka, D.: Speech Corpus Preparation for Voice Banking of Laryngectomised Patients, pp. 282–290. Springer, Cham (2015)
Kramp, B., Dommerich, S.: Tracheostomy cannulas and voice prosthesis. GMS Curr. Top. Otorhinolaryngol. Head Neck Surg. 8, Doc05 (2009)
Liu, H., Ng, M.L.: Electrolarynx in voice rehabilitation. Auris Nasus Larynx 34(3), 327–332 (2007)
Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., et al.: The Kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. No. EPFL-CONF-192584. IEEE Signal Processing Society (2011)
Radová, V., Psutka, J.: UWB-S01 corpus: a Czech read-speech corpus. In: Proceedings of the 6th International Conference on Spoken Language Processing, ICSLP 2000, pp. 732–735 (2000)
Stanislav, P., Psutka, J.V.: Influence of different phoneme mappings on the recognition accuracy of electrolaryngeal speech. In: Proceedings of the International Conference on Signal Processing and Multimedia Applications and Wireless Information Networks and Systems, SIGMAP (ICETE 2012), vol. 1, pp. 204–207 (2012)
Švec, J., Hoidekr, J., Soutner, D., Vavruška, J.: Web Text Data Mining for Building Large Scale Language Modelling Corpus, pp. 356–363. Springer, Heidelberg (2011)
Acknowledgements
The work has been supported by the grant of the University of West Bohemia, project No. SGS-2016-039 and by the Ministry of Education, Youth and Sports of the Czech Republic project No. LO1506.
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Stanislav, P., Psutka, J.V., Psutka, J. (2017). Recognition of the Electrolaryngeal Speech: Comparison Between Human and Machine. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_57
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DOI: https://doi.org/10.1007/978-3-319-64206-2_57
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