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Developed a speech recognition system using TDNN, preprocessing audio, extracting MFCC features, and training the model. Fine-tuning with augmented data (19,000 rows) improved accuracy from 9% to 80% training and 40% validation. Data augmentation proved crucial for enhancing model performance and generalization. Still working to increase the acc.

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Kavayk29/Speech-Recognition-using-TDNN-and-Data-Augmentation

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Developed a speech recognition system using TDNN, preprocessing audio, extracting MFCC features, and training the model. Fine-tuning with augmented data (19,000 rows) improved accuracy from 9% to 80% training and 40% validation. Data augmentation proved crucial for enhancing model performance and generalization. Still working to increase the acc.

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