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Speech-to-Text Application for Stuttering Individuals
Designed a speech to text application that translates stuttered speech into clear, fluent text to support autistic and speech disordered individuals. Analyzed over 28000 audio samples from the SEP 28k dataset and implemented Whisper AI with NumPy and Pandas to train and optimize the model's performance, improving transcription accuracy by 87%.
Key Achievements
- ✓Analyzed over 28,000 audio samples from the SEP 28k dataset
- ✓Improved transcription accuracy by 87%
- ✓Designed to support autistic and speech disordered individuals
Technologies Used
PythonWhisper AIPandasNumPyHugging Face