Emotion Decoding of Speech Using NLP Analysis Approach

Emotion decoding of speech using NLP provides an integrated solution for emotion-driven applications. This project creates a comprehensive system for speech-to-text conversion, sentiment analysis, transcription accuracy evaluation, and senti- ment visualization. The system converts spoken audio into text using the SpeechRecognition library and the Google Web Speech API. To ensure reliability, transcription accuracy is assessed using the Levenshtein distance algorithm, which calculates the smallest number of edits necessary to determine the similarity between the transcribed text and the reference text. VADER (Valence Aware Dictionary and Sentiment Reasoner) from the NLTK library is used by the system for emotion analysis. It is a potent tool for assessing sentiment intensity and assigning sentiment scores to associated emotional states like positive, negative, or neutral. Actionable insights into the speech's emotional content are offered by this analysis. The system creates pie charts using Matplotlib to show sentiment distribution in order to improve interpretability. Users can quickly and naturally understand the emotion landscape thanks to this functionality. This system is well-suited for real-world uses like psychological analysis, where emotional states offer vital diagnostic information, and customer service, where comprehending client emotions is essential. It does this by integrating speech processing, transcription evaluation, sentiment analysis, and visualization into a coherent framework.