Kaldi aims to provide software that is flexible and extensible,[2] and is intended for use by automatic speech recognition (ASR) researchers for building a recognition system.
Kaldi is capable of generating features like mfcc, fbank, fMLLR, etc. Hence in recent deep neural network research, a popular usage of Kaldi is to pre-process raw waveform into acoustic feature for end-to-end neural models.
^Emmanuel Vincent, Jon Barker, Shinji Watanabe, Jonathan Le Roux, Francesco Nesta, et
al.. The second 'CHiME' Speech Separation and Recognition Challenge: Datasets, tasks and
baselines. ICASSP - 38th International Conference on Acoustics, Speech, and Signal Processing
- 2013, May 2013, Vancouver, Canada. pp.126-130, 2013.