Single-chip speech recognition system based on 8051 microcontroller core

Shi, Yuanyuan; Liu, Jia; Runsheng, Liu

doi:10.1109/30.920433

Cited by 30 publications

(4 citation statements)

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“…In the first phase, standard packages and libraries in higherlevel languages are needed to be utilized to develop code for the filtering process of the raw data coming from the array sensors. Here, computational cost must be taken into consideration for implementing into a microcontroller core, which will require optimization of the code due to memory or processing speed limitations [30]. In this respect, principal component analysis can be utilized for dimensionality reduction, and then data scaling is to be performed to increase the rate of convergence of the optimization algorithm implemented in the ANN [31].…”

Section: Implementation In Mobile Devices For Real-time Monitoringmentioning

confidence: 99%

Identification and quantification of gases and their mixtures using GaN sensor array and artificial neural network

Khan

Motayed

Rao

2021

Meas. Sci. Technol.

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Accurate identification and quantification of gas mixtures are almost unattainable utilizing only a metal-oxide/GaN sensor because of its cross-sensitivity to many gases. Here, an array of sensors has been formed consisting of Ag and Pt incorporated ZnO, In2O3 and TiO2 coated two terminal GaN photoconductors. The common environmental toxic gases, such as SO2, NO2, H2, ethanol and their mixtures have been chosen as the gas analytes. All the gas responses have been obtained at 20 °C under UV illumination. Temporal responses have been post-processed to develop the training and test dataset. Then, four different artificial neural network models have been analyzed and optimized for gas classification study, which is done for the first time on GaN sensors. Statistical and computational complexity results indicate that back-propagation neural network (NN) stands out as the optimal classifier among the considered algorithms. Then, ppm concentrations of the identified gases have been estimated using the optimal model. Furthermore, implementation of the developed sensor array in combination with NN algorithm for real-time gas monitoring applications has been discussed.

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Section: Implementation In Mobile Devices For Real-time Monitoringmentioning

confidence: 99%

Identification and quantification of gases and their mixtures using GaN sensor array and artificial neural network

Khan

Motayed

Rao

2021

Meas. Sci. Technol.

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“…The implementation of the speech recognition can be classified as software design and hardware-software codesign method. Yuanyuan et al [1] proposed 8051 single chip to implement the speech recognition. However, its ability of computation is difficult to run more complicated robust speech recognition algorithm.…”

Section: Introductionmentioning

confidence: 99%

Chip Design of LPC-cepstrum for Speech Recognition

Zhu

2007

6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007)

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This paper proposed an ASIC of LPC-cepstrum (LPCC) for speech recognition. The proposed ASIC of LPCC can reduce the calculation load of processor in the speech recognition system. In addition, the resource sharing method is adopted into our design in order to reduce the chip size. Hence, it does not give an emphasis on sophistication but on highperformance and low-cost solution. Finally, we did some experiments to compare with other DSP or ASIC design. We found that our proposed LPCC ASIC can efficiently reduce the computation load.

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“…alanlarda ses ile kontrol işlem-leri için de kullanılabilir. Böylece insan rahatı ve konforunun artırabileceği, pratiklik sağlanabileceği ve verim artışı sağlanabileceği düşünülmektedir [6,20,21]. Sistem modüler ve yazılım yolu ile kontrol edilebilen esnek bir yapıya sahip olup, gerektiğinde tasarımcıların ihtiyacına göre farklı amaçlarla birçok uygulamalarda kullanılabi-lir.…”

Section: Gi̇ri̇ş (Introduction)unclassified