A Method of Mapping a Block of Main Memory to Cache in Parallel Processing of the Speech Signal

Musaev, Muhammadjon; Rakhimov, Mekhriddin

doi:10.1109/icisct47635.2019.9011946

Cited by 14 publications

(3 citation statements)

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“…Novel methods, algorithms, and more modern applications are currently being developed and improved for the segmentation of speech signals and the calculation of parametric indicators of selected fragments, thereby creating a spectrogram of speech signals using spectral analysis [ 4 , 5 , 6 , 7 ]. Speaker identification with diversified voice clips across the globe is a crucial and challenging task, especially in extracting vigorous and discriminative features [ 8 ].…”

Section: Related Workmentioning

confidence: 99%

“…The TBB and OpenMP packages were used to create parallel algorithms for spectral transformations. In these methods, the command divides the signal into frames; for example, N = 16, 32, …, or 4096 for each frame [ 4 ]. The size of each created frame is equal to the block size of the cache memory because cache memory is a factor that affects the efficiency of parallel processing.…”

Section: Pre-processing: Materials Overviewmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Pre-processing: Materials Overviewmentioning

confidence: 99%

Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm

Abdusalomov

Safarov

Rakhimov

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…High-performance hardware is needed for deep learning algorithms that use huge datasets, such as heterogeneous computing systems (M. Rakhimov & M. Ochilov, 2021) or parallel computing techniques. At the moment, parallel and distributed computing technologies (M. Musaev & M. Rakhimov, 2019); (M. Rakhimov, D. Mamadjanov and A. Mukhiddinov, 2020) can also be used to overcome this issue. The major goal of this study is to choose significant parametric variables from the gathered disease data that produce more F1-score outcomes.…”

Section: Introductionmentioning

confidence: 99%

Applying AI in the Healthcare Sector: Difficulties

Garba,

Ahmad,

Bello

2023

CIS

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Artificial intelligence (AI) broadly speaking refers to any behavior shown by a computer or system that is similar to that of a person. Computers can learn from data without explicit human programming thanks to a kind of artificial intelligence known as "machine learning". The application of artificial intelligence (AI) technologies in medicine is one of the most important current trends in global healthcare. Artificial intelligence-based technologies are radically changing the global healthcare system by allowing for a drastic rebuilding of the medical diagnostics system and a corresponding decrease in healthcare costs. Prior to beginning treatment, an illness must be classified into which class of disorders it belongs. It is possible to classify the disease kind according to the feature space of the ailment. Machine learning algorithms can help with this problem.

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