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 is developed on the basis of the Kaldi toolkit. However, Eesen is fully self-contained, requiring no dependencies from Kaldi to funciton.

Eesen is released as an open-source project under the highly non-restrictive Apache License Version 2.0. We welcome community contribution.

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.

Contact

If you have questions, post to Issues or email us.

References

For more informaiton, refer to the following paper(s):

EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding, ASRU 2015.