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ROBUST SPEECH RECOGNITION TECHNIQUES FOR NOISY SURROUNDINGS

Shreya Krishna, Shalini G, Sannidhi UP, Swathi YN

Abstract


In modern years , the accuracy and effectiveness of speech recognition (SR) is the hottest research area by far. It has been tough-going, playing an increasingly important role in many real-world applications.  This method is being practiced in medicine for a several years as they are using it for sake of hearing aids where the performance and advancements has been drastically improved. This study provides a detailed analysis of diverse speech recognition test included two test contents and two  auditory environments: quiet and noise. This research paper focuses on analyzing and improving speech recognition (SR) systems, particularly in noisy environments. It reviews traditional methods like noise reduction and adaptive filtering, alongside deep learning techniques such as convolutional and recurrent neural networks. The study examines the performance of SR systems in various scenarios, including quiet and noisy settings, and explores real-world applications in fields like transportation, offices, and hearing aids. Key challenges, such as handling noise and optimizing signal modelling, are highlighted, with  experiments showing a 30% error reduction compared to state-of-the-art systems.

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References


Tan, C.Y., Tu, L., Wang, Y.H., Jin, D.D.,Y., and Shi, W.D. (2024) were all involved. Using wireless audio streaming in hearing aids to improve speech recognition during phone calls in noisy environments. Journal of the Open Access Library, 11: e11343.

Ear, Nose & Throat Journal 2021, Vol. 100(7) 490–496 ªThe Author(s) 2019 Article reuse guidelines: sagepub.com/journals-permissions Doi.10.1177/0145561319880384 journals.sagepub.com/home/ear.

Elharati, H.A., and V.Z. Këpuska (2015) MFCC, LPCC, PLP, RASTA-PLP, and Hidden Markov Model Classifier are conventional and hybrid features of a robust speech recognition system that works well in noisy environments. Computer and Communications Journal, 3, 1-9.

Effects of Wireless Remote Microphone on Speech Recognition in Noise for Chinese Hearing Aid Users Chen J, Wang Z, Dong R, Fu X, Wang Y, and Wang S (2021). Doi: 10.3389/fnins.2021.643205 Front. Neurosci. 15:643205.

Orimoto, H., Ikuta, A. and Hasegawa, K. (2021) Speech Signal Detection Based on Bayesian Estimation by Observing Air-Conducted Speech under Existence of Surrounding Noise with the Aid of Bone-Conducted Speech. Intelli gent Information Management, 13, 199-213

Barker, J., Vincent, E., Ma, N., Christensen, H., & Green, P. (2013). The Chime corpus: A resource and a challenge for computational hearing in multisource environments. Journal of Acoustical Society of America, 133(5), 3591- 3601.

Barker, J., Vincent, E., Ma, N., Christensen, H., & Green, P. (2013).The Chime corpus: A resource and a challenge for computational hearing in multisource environments. Journal of Acoustical Society of America, 133(5), 3591-3601

Deepak, N.R., Balaji, S. (2016). Uplink Channel Performance and Implementation of Software for Image Communication in 4G Network. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives and Application in Intelligent Systems. CSOC 2016. Advances in Intelligent Systems and Computing, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-319-33622-0_10

Simran Pal R and Deepak N R, “Evaluation on Mitigating Cyber Attacks and Securing Sensitive Information with the Adaptive Secure Metaverse Guard (ASMG) Algorithm Using Decentralized Security”, Journal of Computational Analysis and Applications (JoCAAA), vol. 33, no. 2, pp. 656–667, Sep. 2024.

B, Omprakash & Metan, Jyoti & Konar, Anisha & Patil, Kavitha & KK, Chiranthan. (2024). Unravelling Malware Using Co-Existence Of Features. 1-6. 10.1109/ICAIT61638.2024.10690795.


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