< Description />
β Developed and implemented audio sentiment analysis models to detect and classify emotions in
speech using natural language processing (NLP) and machine learning techniques.
β Utilized libraries and tools such as Python, TensorFlow, PyTorch and OpenAI's Whisper for
speech-to-text conversion and sentiment classification.
β Processed and analyzed large datasets of audio files to extract sentiment features such as tone, pitch,
and intensity, contributing to improved customer experience and feedback analysis.
β Conducted model evaluation and tuning to achieve high accuracy in sentiment classification across
diverse languages and dialects.
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