Accent Classifier – Multilingual Speech Pattern Recognition
Project Overview: The Accent Classifier is a machine learning system designed to identify and categorize speaker accents from audio input. It leverages deep learning models trained on multilingual speech datasets to support applications in voice personalization, accessibility, and forensic linguistics.
Key Components:
Audio Preprocessing:
Noise reduction, silence trimming, and MFCC (Mel-frequency cepstral coefficients) extraction
Spectrogram generation for CNN-based classification
Model Architecture:
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for temporal pattern recognition
Fine-tuned transformer-based models (e.g., Wav2Vec 2.0) for high-accuracy accent embeddings
Training Pipeline:
Labeled datasets across regional English accents (e.g., Kenyan, British, Indian, American)
Data augmentation via pitch shifting and time stretching to improve generalization
Evaluation using precision, recall, F1-score, and confusion matrix analysis
Deployment:
Real-time inference via serverless architecture (AWS Lambda + S3)
REST API integration for voice-enabled applications and chatbot personalization
Use Cases:
Accent-aware transcription and translation
Adaptive voice interfaces for global users
Forensic audio analysis in multilingual investigations pitchdeck…Accent Classifier – Multilingual Speech Pattern Recognition
Project Overview: The Accent Classifier is a machine learning system designed to identify and categorize speaker accents from audio input. It leverages deep learning models trained on multilingual speech datasets to support applications in voice personalization, accessibility, and forensic linguistics.
Key Components:
Audio Preprocessing:
Noise reduction, silence trimming, and MFCC (Mel-frequency cepstral coefficients) extraction
Spectrogram generation for CNN-based classification
Model Architecture:
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for temporal pattern recognition
Fine-tuned transformer-based models (e.g., Wav2Vec 2.0) for high-accuracy accent embeddings
Training Pipeline:
Labeled datasets across regional English accents (e.g., Kenyan, British, Indian, American)
Data augmentation via pitch shifting and time stretching to improve generalization
Evaluation using precision, recall, F1-score, and confusion matrix analysis
Deployment:
Real-time inference via serverless architecture (AWS Lambda + S3)
REST API integration for voice-enabled applications and chatbot personalization
Use Cases:
Accent-aware transcription and translation
Adaptive voice interfaces for global users
Forensic audio analysis in multilingual investigations pitchdeckWW…