Abstract
Based on the literature, it is possible to build voice recognition systems by using voice synthesizers and voice command controls. In addition, phonemes recognition can be made by implementing algorithms already created for this kinds of tasks. Nevertheless, phonemes recognition might generate some errors, when the implementation of such algorithms is unsuitable. Then, the possibility to perform phonemes recognition based on open source APIs arises. In the work presented in this paper, we used open source APIs for voice commands recognition. Thus, we propose an architecture that allows the construction of a system for phonemes recognition and voice synthesizers. The results have been implemented and validated in order to illustrate the reliability of the proposed architecture.
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References
Huang, X., Acero, A., Hon, H.W., Reddy, R.: Spoken Language Processing: A Guide To Theory, Algorithm, and System Development, vol. 95. Prentice hall PTR, Upper Saddle River (2001)
He, X., Deng, L.: Speech-centric information processing: an optimization-oriented approach. Proc. IEEE 101(5), 1116–1135 (2013)
Deng, L., et al.: Distributed speech processing in mipad’s multimodal user interface. IEEE Trans. Speech Audio Process. 10(8), 605–619 (2002)
Kumatani, K., McDonough, J., Raj, B.: Microphone array processing for distant speech recognition: from close-talking microphones to far-field sensors. IEEE Signal Process. Mag. 29(6), 127–140 (2012)
Zhang, B., Gan, Y., Song, Y., Tang, B.: Application of pronunciation knowledge on phoneme recognition by LSTM neural network. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2906–2911. IEEE (2016)
Karan, G., Kumar, D., Pai, K., Manikandan, J.: Design of a phoneme based voice controlled home automation system. In: 2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 31–35. IEEE (2017)
Grossinho, A., Guimaraes, I., Magalhaes, J., Cavaco, S.: Robust phoneme recognition for a speech therapy environment. In: 2016 IEEE International Conference on Serious Games and Applications for Health (SeGAH), pp. 1–7. IEEE (2016)
Jahan, M., Khan, M.: Sub-vocal phoneme-based EMG pattern recognition and its application in diagnosis. In: 2015 Annual IEEE India Conference (INDICON), pp. 1–4. IEEE (2015)
Wu, T., Yang, Y., Wu, Z., Li, D.: Masc: a speech corpus in mandarin for emotion analysis and affective speaker recognition. In: IEEE Odyssey 2006: The Speaker and Language Recognition Workshop, pp. 1–5. IEEE (2006)
Ichino, M., Sakano, H., Komatsu, N.: Text-indicated speaker recognition using kernel mutual subspace method. In: 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008, 957–961. IEEE (2008)
Miyuki, Y., Hagiwara, Y., Taniguchi, T.: Unsupervised learning for spoken word production based on simultaneous word and phoneme discovery without transcribed data. In: 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 156–163. IEEE (2017)
Kharchenko, O., Raichev, I., Bodnarchuk, I., Zagorodna, N.: Optimization of software architecture selection for the system under design and reengineering. In: 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), pp. 1245–1248. IEEE (2018)
Hochgeschwender, N., Biggs, G., Voos, H.: A reference architecture for deploying component-based robot software and comparison with existing tools. In: 2018 Second IEEE International Conference on Robotic Computing (IRC), pp. 121–128. IEEE (2018)
Deng, L., O’Shaughnessy, D.: Speech Processing: A Dynamic and Optimization-Oriented Approach. CRC Press (2003)
Acero, A.: Acoustical and Environmental Robustness in Automatic Speech Recognition, vol. 201. Springer Science & Business Media (2012)
Kolossa, D., Haeb-Umbach, R. (eds.): Robust Speech Recognition of Uncertain or Missing Data: Theory and Applications. Springer Science & Business Media, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21317-5
Deng, L.: Front-end, back-end, and hybrid techniques for noise-robust speech recognition. In: Kolossa, D., Häb-Umbach, R. (eds.) Robust Speech Recognition of Uncertain or Missing Data, pp. 67–99. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21317-5_4
Hualde, J.I.: The Sounds of Spanish with Audio CD. Cambridge University Press (2005)
Dziadzio, S., Nabożny, A., Smywiński-Pohl, A., Ziółko, B.: Comparison of language models trained on written texts and speech transcripts in the context of automatic speech recognition. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 193–197. IEEE (2015)
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Wanumen, L., Florez, H. (2018). Architectural Approaches for Phonemes Recognition Systems. In: Florez, H., Diaz, C., Chavarriaga, J. (eds) Applied Informatics. ICAI 2018. Communications in Computer and Information Science, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-01535-0_20
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DOI: https://doi.org/10.1007/978-3-030-01535-0_20
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