Emotions are a vital and fundamental part of our existence. Whatever we do, say, or do not say somehow reflects our feelings, however not immediately. To comprehend human’s most fundamental behaviour, we must examine these feelings using emotional data. According to the extensive literature review, categorising speech text into multiple classes is now undergoing extensive investigation. The application of this research is very limited in local and regional languages such as Hindi. This study focuses on text emotion analysis, specifically for the Hindi language. In our study, BHAAV Dataset is used, which consists of 20,304 sentences, where every other sentence has been manually annotated into one of the five emotion categories (Anger, Suspense, Joy, Sad, Neutral). Comparison of multiple machine learning and deep learning techniques with word embedding is used to demonstrate accuracy. And then, the trained model is used to predict the emotions of Hindi text. The best performance were observed in case of mBERT model with loss- 0.1689 ,balanced_accuracy- 93.88%, recall- 93.44%, auc- 99.55% and precision- 94.39 % on training data, while loss- 0.3073, balanced_accuracy- 91.84%, recall- 91.74%, auc- 98.46% and precision- 92.01% on testing data.

Kumar, Tapesh Mahrishi, Mehul Nawaz, SarfarazOver recent decades, sentiment analysis has been progressive. A lot of effort focused on text analysis using techniques of text mining. However, audio sentiment analysis is still in the developing phase in the academic community. In this recommended study, we examine different transcripts of speech sentiment also with speaker recognition to assess speakers’ emotions. The study of the sentiment of speech in many businesses, such as customer service, health care, and education, represents a major issue.

Speech has been the most popular form of human communication. A keyboard or a mouse, on the other hand, is the most common way of entering data into a computer. It would be wonderful if computers could understand and carry out human commands. The method of obtaining the transcription (word sequence) of an utterance from the speech waveform is known as automatic speech recognition (ASR). Over the last few decades, speech technology and systems in human-computer interaction have progressed progressively and significantly. This chapter suggests a comprehensive review of automatic speech recognition systems (ASR) and their most recent developments. This research aims to outline and explain some of the popular approaches in speech recognition systems at various stages and highlight selected systems' unique and innovative characteristics.