Emolah: A Malay Language Spontaneous Speech Emotion Recognition on iOS Platform - Ramli, Izzad; Jamil, Nursuriati; Ardi, Norizah; Hamzah, Raseeda (International Journal of Engineering and Technology, 2018)
This paper presented the implementation of spontaneous speech emotion recognition (SER) using smartphone on iOS platform. The novelty of this work is at the time of writing, no similar work has been done using Malay language spontaneous speech. The development of SER using a mobile device is important for ease of use anytime and anywhere. The main factors to be considered is the computational complexity of classifying the emotions in real-time. Therefore, we introduced EmoLah, a Malay language spontaneous SER that is able to recognize emotions on the go with satisfactory accuracy rate. Pitch and energy prosody features are used to represent the emotions in the spontaneous speech and NaÃ¯ve Bayes learning model is selected as the classifier. EmoLah is trained and tested using Malay language spontaneous speech acquired from television talk shows, live interviews from news broadcast and mini-parliament sessions conducted by children. Four types of speech emotions are collected that are happy, sad, angry and neutral. The total duration of all the speech emotion is four hours. The speech emotion training is using MATLAB scripts and the weights are implemented in XCODE as the iOS software for application development. Emolah accuracy is evaluated using cross-validation test and the result showed that it can discriminate angry, sad and happy. However, most emotions are misclassified as neutral emotion.Â Â