Audio-Visual Speech Recognition using 3D Convolutional Neural Networks

Belhan, Ceren; Fikirdanis, Damla; Cimen, Ovgu; Pasinli, Pelin; Akgun, Zeynep; Yayci, Zeynep Ovgu; Türkan, Mehmet

doi:10.1109/asyu52992.2021.9599016

Cited by 2 publications

(1 citation statement)

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“…Future studies will focus on improving the accuracy of our method by using deep learning approaches and optimizing the cache memory of multicore processors to detect and extract speech signals without a significant quality loss. Furthermore, we plan to construct a spectral analysis model based on parallel processing with robust analysis performance that will enable the establishment of embedded devices with low computational resources using Taris speech datasets [ 70 ] in the 3D CNN and 3D U-Net Environment [ 71 , 72 , 73 , 74 , 75 ].…”

Section: Discussionmentioning

confidence: 99%

Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm

Abdusalomov

Safarov

Rakhimov

et al. 2022

Sensors

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Speech recognition refers to the capability of software or hardware to receive a speech signal, identify the speaker’s features in the speech signal, and recognize the speaker thereafter. In general, the speech recognition process involves three main steps: acoustic processing, feature extraction, and classification/recognition. The purpose of feature extraction is to illustrate a speech signal using a predetermined number of signal components. This is because all information in the acoustic signal is excessively cumbersome to handle, and some information is irrelevant in the identification task. This study proposes a machine learning-based approach that performs feature parameter extraction from speech signals to improve the performance of speech recognition applications in real-time smart city environments. Moreover, the principle of mapping a block of main memory to the cache is used efficiently to reduce computing time. The block size of cache memory is a parameter that strongly affects the cache performance. In particular, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in speech recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from speech signals. Problems with overclocking during the digital processing of speech signals have yet to be completely resolved. The experimental results demonstrate that the proposed method successfully extracts the signal features and achieves seamless classification performance compared to other conventional speech recognition algorithms.

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