Intelligent Recognition Mobile Platform Based on STM32

In the field of human-machine interaction, automatic speech recognition (ASR) has been a prominent research area since the 1950s. Single-word speech recognition is widely used in voice command systems, which can be implemented in various applications such as access control systems, robots, and voice-enabled devices. This study describes the implementation of a single-word speech recognition system using wave atoms transform (WAT) and frequency-mel cepstral coefficients (MFCC) on a Raspberry Pi 3 (RPi 3) board. The WAT-MFCC approach is combined with a support vector machine (SVM). The experiment was conducted on an Arabic word database, and the results showed that the proposed WAT-MFCC-SVM method is highly reliable, achieving a detection rate of 100% and a real-time factor (RTF) of 1.50.

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Intelligent Recognition Mobile Platform Based on STM32

Cited by 2 publications

References 2 publications

Intelligent Garbage Classification System Based on Open MV

Intelligent Garbage Classification System Based on Open MV

Speech Recognition System Implementation of a Method Based on Wave Atom Transform and Frequency-Mel Cepstral Coefficients Using SVM

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