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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.

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