Verbal Abuse Detection from Short Conversations

Smart assistants and smart microphones can contribute to detecting verbal abuse by analyzing speech-to-text contents. In this paper, we compare different large language models (LLMs) for detecting verbal abuse and propose a framework that first identifies emotion from short audio conversations by extracting Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram (MEL) features. It then utilizes transfer learning with a fully connected neural network incorporating an attention mechanism and SBERT encoding. To demonstrate the efficacy of the proposed framework, we prepared a custom dataset containing instances of verbal abuse. Evaluation results show that our framework is lightweight and achieves commendable accuracy compared to the existing LLM models.