mravanelli/pytorch_MLP_for_ASR
This code implements a basic MLP for speech recognition. The MLP is trained with pytorch, while feature extraction, alignments, and decoding are performed with Kaldi. The current implementation supports dropout and batch normalization. An example for phoneme recognition using the standard TIMIT dataset is provided.
This project helps speech recognition researchers and engineers develop and evaluate acoustic models for HMM-DNN speech recognition systems. It takes speech features and alignments generated by Kaldi and uses them to train a Multi-Layer Perceptron (MLP) acoustic model with PyTorch. The output is a trained MLP model that can be integrated into a larger speech recognition pipeline for tasks like phoneme recognition.
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Use this if you are a speech recognition researcher or engineer looking to train a basic MLP acoustic model using PyTorch, with feature extraction and decoding handled by Kaldi.
Not ideal if you are looking for a complete, end-to-end speech recognition solution that does not require prior Kaldi expertise or existing Kaldi-generated features and alignments.
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Last pushed
Feb 10, 2018
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