Eesen
Eesen is to simplify the existing complicated, expertise-intensive ASR pipeline into a straightforward learning problem. Acoustic modeling in Eesen involves training a single recurrent neural network (RNN) to model the mapping from speech to transcripts. Eesen discards the following elements required by the existing ASR pipeline:
- Hidden Markov models (HMMs)
- Gaussian mixture models (GMMs)
- Decision trees and phonetic questions
- Dictionary, if characters are used as the modeling units
- ...
Eesen was created by Yajie Miao based on the Kaldi toolkit.
Key Components
Eesen contains 3 key components to enable end-to-end ASR:
- Acoustic Model -- Bi-directional RNNs with LSTM units.
- Training -- Connectionist temporal classification (CTC) as the training objective.
- Decoding -- A principled decoding approach based on Weighted Finite-State Transducers (WFSTs).
Highlights of Eesen
- The WFST-based decoding approach can incorporate lexicons and language models into CTC decoding in an effective and efficient way.
- GPU implementation of LSTM model training and CTC learning.
- Multiple utterances are processed in parallel for training speed-up.
- Fully-fledged example setups to demonstrate end-to-end system building, with both phonemes and characters as labels.
Experimental Results
Refer to RESULTS under each example setup.
References
For more information, please refer to the following paper(s):
Yajie Miao, Mohammad Gowayyed, and Florian Metze, "EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding," in Proc. ASRU 2015.