Abstract
In recent times, voice emotional recognition has been established as a significant field in Human Computer Interaction (HCI). Human emotions are intrinsically expressed by linguistic (verbal content) information and paralinguistic information such as tone, emotional state, expressions and gestures which form the relevant basis for emotional analysis. Accounting the user’s affective state in HCI systems is essential to detect subtle user behaviour changes through which the computer can initiate interactions instead of simply responding to user commands. This paper aims to tackle existing problems in speech emotion recognition (SER) by taking into account the acoustic cues and prosodic parameters to detect user emotion. Here, the work will be mainly on the Ryerson Audio-Visual Database (RAVDESS) to extract Mel Frequency Cepstral Coefficients (MFCC) from signals to recognise emotion. The librosa library will be used for processing and extracting audio files before testing and classification is carried out using 4 different classifiers for a comparative study. This SER model is then integrated into a Telegram Bot to develop an intuitive, user-friendly interface as an application for psychological therapy.
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Nag, I., Syed, S.A., Basu, S., Shaw, S., Banik, B.G. (2022). Telegram Bot for Emotion Recognition Using Acoustic Cues and Prosody. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_31
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