Abstract
Most of the speech communication applications viz. telephony, hands-free communication, voice recording, automatic speech recognition, interactive voice response system, human-machine interfaces, etc. that require at least one microphone, desired speech signal is usually contaminated by background noise and reverberation. As a result, the speech signal has to be “cleaned” with digital signal processing tools before it is played out, transmitted, or stored. The noise estimation and reduction techniques will help to clean and attenuate the noise component in speech data, known as Speech Enhancement. In this paper, we recorded the speech in different environmental conditions and estimated the noise signal/background noise distribution in speech. Now the speech is enhanced by using the compliment of Weiner-Hopf optimal filter. And this enhanced speech signal is given for training and testing the Automatic Speech Recognition (ASR) system, which will improve the word accuracy. The analysis of speech and results presented in this paper are produced using MATLAB.
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References
Veisi, H., Sameti, H.: Hidden-Markov-model-based voice activity detector with high speech detection rate for speech enhancement. IET Sig. Process. 6(1), 54–63 (2012)
Kashiwagi, Y., Suzuki, M., Minematsu, N., Hirose, K.: Audio-Visual feature interation based on piecewise linear transformation for noise robust automatic speech recognition. In: ICASSP, pp. 149–152. IEEE (2010)
Ghaemmaghami, H., Dean, D., Sridharan, S., McCown, I.: Noise robust voice activity detection using normal testing and time-domain histogram analysis. In: ICASSP, pp. 4470–4473. IEEE (2010)
Dhananjaya, N., Yegnanarayana, B., Senior Member, IEEE: Voiced/nonvoiced detection based on robustness of voiced epochs. IEEE Sig. Process. Lett. 17(3) (2010)
Benesty, J., Chen, J., Huang, Y., Cohen, I.: Noise reduction in speech processing. Springer, Heidelberg (2009)
Kim, D.Y., Un, C.K., Kim. N.S.: Speech recognition in noisy environments using first-order vector Taylor series. Speech Commun. 24, 39–49 (1998)
Ramirez, J., Yelamos, P., Gorriz, J.M., Segura, J.C.: SVM-based speech endpoint detection using contextual speech features. Electron. Lett. 42(7), 426–428 (2006)
Sebastian Seung, H.: Wiener-Hopf equations. Convolution and correlation in continuous time, 9.29 Lecture 3: February 11, 2003 (2003)
Verma, A.R., Singh, R.K., Kumar, A., Ranjeet, K.: An improved method for speech enhancement based on 2D-DWT using hybrid weiner filtering. In: 2012 IEEE International Conference on Computational Intelligence and Computing Research (2012)
Samudravijaya, K., Barot, M.: A comparison of public domain software tools for speech recognition. Workshop on spoken language processing, pp. 125–131 (2003)
Fukane, A.R., Sahare, S.L.: Role of noise estimation in enhancement of noisy speech signals for hearing aids. In: Computational Intelligence and Communication Networks (CICN), pp.648–652. IEEE (2011)
Ling, G., Yamada, T., Makino, S., Kitawaki, N.: Performance estimation of noisy speech recognition using spectral distortion and snr of noise-reduced speech. In: TENCON 2013, IEEE (2013)
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Kishore, N.S.B., Venkata, M.R., Nagamani, M. (2015). Environmental Noise Analysis for Robust Automatic Speech Recognition. In: Sulaiman, H., Othman, M., Othman, M., Rahim, Y., Pee, N. (eds) Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-07674-4_75
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DOI: https://doi.org/10.1007/978-3-319-07674-4_75
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