Advances in Artificial Intelligence Using Speech Recognition

Authors: Khaled M. Alhawiti

Abstract:

This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: Speech recognition, acoustic phonetic, artificial intelligence, Hidden Markov Models (HMM), statistical models of speech recognition, human machine performance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1106879

References:

[1] Ammar, Hany H., Walid Abdelmoez, and Mohamed Salah Hamdi. "Software engineering using artificial intelligence techniques: Current state and open problems." Proceedings of the First Taibah University International Conference on Computing and Information Technology (ICCIT 2012), Al-Madinah Al-Munawwarah, Saudi Arabia. (2012), p. 52, retrieved from, http://www.researchgate.net/profile/ Mohamed_Hamdi8/publication/254198356_Software_Engineering_Usin g_Artificial_Intelligence_Techniques_Current_State_and_Open_Proble ms/links/544771110cf2f14fb811f118.pdf
[2] Anusuya, A.M. and Katti, K.S. “Speech Recognition by Machine: A Review”, (IJCSIS) International Journal of Computer Science and Information Security, (2009), p. 7, retrieved from, http://arxiv.org/ftp/arxiv/papers/1001/1001.2267.pdf
[3] Beigi, Homayoon. "Hidden Markov Modeling (HMM)." Fundamentals of Speaker Recognition. Springer, (2011), p. 41, retrieved from, http://link.springer.com/chapter/10.1007/978-0-387-77592-0_13
[4] Besacier, Laurent, et al. "Automatic speech recognition for underresourced languages: A survey." Speech Communication, (2014), p. 100, retrieved from, http://www.sciencedirect.com/science/article/pii/ S0167639313000988
[5] Chen, Chi-hau, ed. Pattern recognition and artificial intelligence. Elsevier, (2013), p. 6, retrieved from, https://books.google.com/books? hl=en&lr=&id=QixXL8sxJgEC&oi=fnd&pg=PP1&dq=SPEECH+REC OGNITION+SYSTEM+and+artificial+intelligence&ots=Z7cz1bl- YF&sig=9qQWy2oto_ijLb9Dig1Onby7M5E#v=onepage&q=SPEECH %20RECOGNITION%20SYSTEM%20and%20artificial%20intelligenc e&f=false
[6] Chen, Lijiang, et al. "Speech emotion recognition: Features and classification models." Digital signal processing 22.6 (2012), p. 15, retrieved from, http://www.sciencedirect.com/science/article/pii/ S1051200412001133
[7] Choudhary, A. and Kshirsagar, R. “Process Speech Recognition System using Artificial Intelligence Technique, International Journal of Soft Computing and Engineering (IJSCE), (2012), p. 3, retrieved from, http://www.ijsce.org/attachments/File/v2i5/E1054102512.pdf
[8] Dalby, Jonathan, and Diane Kewley-Port. "Explicit pronunciation training using automatic speech recognition technology." Calico Journal, (2013), p. 22, retrieved from, http://www.equinoxpub.com/journals/ index.php/CALICO/article/viewArticle/23361
[9] Deng, Li, and Xiao Li. "Machine learning paradigms for speech recognition: An overview." IEEE Transactions on Audio, Speech and Language Processing21.5 (2013), p. 45, retrieved from, http://131.107.65.14/pubs/189008/tasl-deng-2244083-x_2.pdf
[10] Hinton, Geoffrey, et al. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." Signal Processing Magazine, IEEE 29.6 (2012), pp. 82-97, retrieved from, http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6296526&url=ht tp%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumb er%3D6296526
[11] Mikolov, Tomas. "Statistical language models based on neural networks." Presentation at Google, Mountain View, (2012), p. 7, retrieved from, http://www.fit.vutbr.cz/~imikolov/rnnlm/google.pdf
[12] Morgan, Nelson. "Deep and wide: Multiple layers in automatic speech recognition." Audio, Speech, and Language Processing, IEEE Transactions on20.1 (2012), p. 6, retrieved from, http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5714717&url=ht tp%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumb er%3D5714717
[13] Rawat, Seema, Parv Gupta, and Praveen Kumar. "Digital life assistant using automated speech recognition." IEEE, (2014), p. 13, retrieved from.http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7019075&u rl=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farn umber%3D7019075.
[14] Saini, Preeti, and Parneet Kaur. "Automatic Speech Recognition: A Review." International journal of Engineering Trends & Technology (2013), pp. 132-136, retrieved from, http://www.ijettjournal.org/volume-4/issue-2/IJETT-V4I2P210.pdf
[15] Saon, George, and Jen-Tzung Chien. "Large-vocabulary continuous speech recognition systems: A look at some recent advances." Signal Processing Magazine, IEEE 29.6 (2012), p. 18, retrieved from, http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6296522&url=ht tp%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumb er%3D6296522.