< Description />
● Developed and implemented audio sentiment analysis models to detect and classify emotions in
speech using natural language processing (NLP) and machine learning techniques.
● Utilized libraries and tools such as Python, TensorFlow, PyTorch and OpenAI's Whisper for
speech-to-text conversion and sentiment classification.
● 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.
● Conducted model evaluation and tuning to achieve high accuracy in sentiment classification across
diverse languages and dialects.
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As a passionate and dedicated software developer, I specialize in creating efficient, scalable, and user-friendly solutions across various domains. With a strong foundation in programming languages like JavaScript, Python, and Java, I am proficient in front-end and back-end deve…