Emotion Recognition From Speech and Text using Long Short-Term Memory

Abstract

Everyday interactions depend on more than just rational discourse; they also depend on emotional reactions. Having this information is crucial to making any kind of practical or even rational decision, as it can help to better understand one another by sharing our responses and providing recommendations on how they may feel. Several studies have recently begun to focus on emotion detection and labeling, proposing different methods for organizing feelings and detecting emotions in speech. Determining how emotions are conveyed through speech has been given major emphasis in social interactions during the last decade. However, the real efficiency of identification needs to be improved because of the severe lack of data on the primary temporal link of the speech waveform. Currently, a new approach to speech recognition is recommended, which couples structured audio information with long-term neural networks to fully take advantage of the shift in emotional content across phases. In addition to time series characteristics, structural speech features taken from the waveforms are now in charge of maintaining the underlying connection between layers of the actual speech. There are several Long-Short-Term Memory (LSTM) based algorithms for identifying emotional focus over numerous blocks. The proposed method (i) reduced overhead by optimizing the standard forgetting gate, reducing the amount of required processing time, (ii) applied an attention mechanism to both the time and feature dimension in the LSTM's final output to get task-related information, rather than using the output from the prior iteration of the standard technique, and (iii) employed a powerful strategy to locate the spatial characteristics in the final output of the LSTM to gain information, as opposed to using the findings from the prior phase of the regular method. The proposed method achieved an overall classification accuracy of 96.81%.

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