This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning.

The paper aims to propose “Portable NIRS Technology for Thai Food Industry” with applications using non-destructive based technology called Near-Infrared Spectroscopy (NIRS) for determining whether the sampled food is expired or not. This proposed NIRS technology will be applicable especially for the agricultural and food industries. The basic process of the proposed system can be divided into the following two parts, i.e., 1) a sensor device that detects samples and 2) a mobile application developed for connecting to the NIRS device. The binary k-nearest neighbors algorithm (k-NN) is proposed to classify the five common Thai local samples, i.e., soy milk (SM), fresh milk (FM), salad dressing (SD), corn milk (CM), and banana in coconut milk (BM). The output results are classified into two classes, i.e., Unexpired (Un) and expired (Ex), based on the wavelength measuring from the proposed NIRS system.

This paper introduces an innovative mobile-assisted language learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex's interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance assessment accuracy and provide nuanced feedback, we integrate a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. Experimental results demonstrate that our multi-feature model achieves 87% accuracy in accent classification across diverse linguistic contexts. User testing revealed statistically significant improvements in pronunciation skills, with learners showing 5-20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning.