Investigation of Speech and Vocal Recognition Motion Techniques
DOI:
https://doi.org/10.1229/tecempresarialjournal.v19i1.419Keywords:
Human-computer interaction, gesture recognition, motion techniques, speech recognition, and voice recognition.Abstract
Recent significant advancements in voice and speech recognition technologies have revolutionized human-computer interaction across multiple domains. While most traditional methods use audio signals for recognition, newer approaches incorporate motion data to improve user experience, robustness, and accuracy. With an emphasis on the incorporation of motion techniques, this research paper offers a thorough analysis of the most recent advancements in speech and voice recognition technology. We examine the development of voice recognition systems, going over their fundamental ideas and uses. We also explore the use of motion data to augment audio-based recognition techniques, such as gestures, facial expressions, and body movements. We examine cutting-edge motion-based algorithms and systems, emphasizing both their advantages and disadvantages. We also look at real-world applications in a variety of sectors, including virtual reality, gaming, healthcare, and the automobile industry.
References
Jurafsky, D., and Martin, J. H. (2009) are cited. Speech and language processing: An
overview of speech recognition, computational linguistics, and natural language processing. Pearson Education.
Hon, H. W., Acero, A., and Huang, X. (2001). A guide to theory, algorithms, and system development for spoken language processing. PTR for Prentice Hall.
Young, S., Liu, X., Kershaw, D., Evermann, G., Gales, M., Hain, T., & Woodland, P.
(2006). For HTK version 3.4, the HTK book. Cambridge University, Department of
Engineering.
Juang, B. H., and Rabiner, L. R. (1993). the principles of voice recognition. Pearson
Education.
Dahl (2012), Yu (2012), Deng (2012), and Acero (2012). Pre-trained deep neural networks
with context dependence for large-vocabulary speech recognition. IEEE Trans. on Speech,
Language, and Audio Processing, 20(1), 30-42.
Hinton, G., Jaitly, N., Mohamed, A. R., Dahl, G. E., Deng, L., Yu, D., & Kingsbury, B.
(2012). Four research groups' common views on deep neural networks for acoustic modeling
in speech recognition. Journal of IEEE Signal Processing, 29(6), 82–97.
Schmidhuber, J., Fernández, S., Gomez, F., and Graves, A. (2006). Recurrent neural
networks are used to label unsegmented sequence data in connectionist temporal
classification. In 23rd International Conference on Machine Learning Proceedings (pp. 369-
.
Cho, K., Bengio, Y., Serdyuk, D., Bahdanau, D., and Chorowski, J. (2015). Speech
recognition models based on attention. (pp. 577–585) in Advances in Neural Information
Processing Systems.
Salakhutdinov, R., and G. Hinton (2006). using neural networks to reduce the dimensionality
of data. 313(5786): 504-507 in Science.
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