entonytang / feature-extraction-for-speech-recognition

Python Implementation of the Feature Extraction Process in Kaldi

Feature-Extraction-for-Speech-Recognition

Python Implementation of the Feature Extraction Process in Kaldi

Brief Discription

This repository contains Python scripts for the feature extraction process in speech recognition systems. It is originated from Kaldi (https://github.com/kaldi-asr/kaldi), but may be more flexible for most speech recognition systems, especially end-to-end ones.

Contents

1. feature_extraction_template.py: a template for the feature extraction process, blocks which need to be filled by the user are commented with the keyword "BLOCK"
2. utils/extract_window.py: the preprocessing and window slicing functions
3. utils/fft2melmx.py: an adopted script calculating mel weights for the conversion from the fft feature to the mel feature, see the comments in the file for details
4. utils/deltas.py: the delta feature calculation function used in TensorFlow graphs
5. utils/deltas_np.py: the delta feature calculation function which can be used without TensorFlow

Arguments

Most of the arguments can be modified in feature_extraction_template.py. The default arguments in the template are used for the wide residual BLSTM network (WRBN) based acoustic model.

Known Limitations and Comments

1. The feature_extraction_template.py only shows the feature extraction process with the usage of TensorFlow. It should be easy to extend it to the version without TensorFlow (using utils/deltas_np.py).
2. Currently the log energy pre window function in Kaldi is not supported. This function is seldomly used in most of my work though. Contributions are welcome.
3. Currently we only support filter-bank (fbank) features. It should be easy to extend to other popular features such as MFCC.