publication . Thesis . 2016

On-device mobile speech recognition

Mustafa, MK;
Open Access English
  • Published: 01 May 2016
  • Country: Italy
Despite many years of research, Speech Recognition remains an active area of research in Artificial Intelligence. Currently, the most common commercial application of this technology on mobile devices uses a wireless client – server approach to meet the computational and memory demands of the speech recognition process. Unfortunately, such an approach is unlikely to remain viable when fully applied over the approximately 7.22 Billion mobile phones currently in circulation. In this thesis we present an On – Device Speech recognition system. Such a system has the potential to completely eliminate the wireless client-server bottleneck. For the Voice Activity Detect...
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21 references, page 1 of 2

M. K. Mustafa, T. Allen, and K. Appiah. A novel K-means Voice Activity Detection Algorithm using Linear Cross correlation on the standard deviation of Linear Predictive Coding, in M. Bramer, M. Petridis (eds.), Research and Development in Intelligent Systems XXXII, Springer International Publishing, 2015. (Best Application Paper).

29. T. Tashan. Biologically Inspired Speaker Verification. Submitted to Nottingham Trent University, November 2012.

30. J. W. Cooley and J. W. Tukey, An algorithm for the machine calculation of complex Fourier series, Mathematics of computation, vol. 19, pp. 1965, 297-301. [OpenAIRE]

31. P. Vaidyanathan, The theory of linear prediction, Synthesis Lectures on Signal Processing, vol. 2, pp. 2007, 1-184.

32. D. L. Jones, S. Appadwedula, M. Berry, M. Haun, J. Janovetz, M. Kramer, D. Moussa, D. Sachs, and B. Wade, Speech Processing: Theory of LPC Analysis and Synthesis, , pp. 2009.

33. T. Kamm, H. Hermansky, and A. G. Andreou, Learning the Mel-scale and optimal VTN mapping: Center for Language and Speech Processing, Workshop (WS 1997). Johns Hopkins University, Citeseer, 1997. [OpenAIRE]

34. Hai, Jiang, and Er Meng Joo. "Improved linear predictive coding method for speech recognition." Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on. Vol. 3. IEEE, 2003.

36. Doniec, Marek W., Brian Scassellati, and Willard L. Miranker. "Emergence of language-specific phoneme classifiers in self-organized maps." Neural Networks, 2007. IJCNN 2007. International Joint Conference on. IEEE, 2007. [OpenAIRE]

37. Gardón, A. Postigo, C. Ruiz Vázquez, and A. Arruti Illarramendi. "Spanish phoneme classification by means of a hierarchy of kohonen self-organizing maps." Text, Speech and Dialogue. Springer Berlin Heidelberg, 1999.

38. Szczurowska, Izabela, Wiesława Kuniszyk-Jóźkowiak, and Elżbieta Smołka. "The application of Kohonen and Multilayer Perceptron Networks in the speech nonfluency analysis." Archives of Acoustics 31.4 (S) (2014): 205-210.

39. Rahman, Md Mijanur, and Md Al-Amin Bhuiyan. "DYNAMIC THRESHOLDING ON SPEECH SEGMENTATION." International Journal of Research in Engineering and Technology. doi: 10.15623/ijret.2013.0209061 [OpenAIRE]

40. J. Wilpon and L. Rabiner, A modified K-means clustering algorithm for use in isolated work recognition, Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 33, pp. 1985, 587-594.

41. H. Yin, The self-organizing maps: background, theories, extensions and applications, Computational intelligence: A compendium, Springer, 2008.

42. K. R. Žalik, An efficient k′-means clustering algorithm, Pattern Recog. Lett., vol. 29, pp. 2008, 1385-1391. [OpenAIRE]

43. C. G. Looney, A fuzzy clustering and fuzzy merging algorithm, CS791q Class notes, , pp. 1999.

21 references, page 1 of 2
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