On Language Model Integration for RNN Transducer Based Speech Recognition
IEEE International Conference on Acoustics Speech and Signal Processing, pp. 8407–8411
Abstract
The mismatch between an external language model (LM) and the implicitly learned internal LM (ILM) of RNN-Transducer (RNN-T) can limit the performance of LM integration such as simple shallow fusion. A Bayesian interpretation suggests to remove this sequence prior as ILM correction. In this work, we study various ILM correction-based LM integration methods formulated in a common RNN-T framework. We provide a decoding interpretation on two major reasons for performance improvement with ILM correction, which is further experimentally verified with detailed analysis. We also propose an exact-ILM training framework by extending the proof given in the hybrid autoregressive transducer, which enables a theoretical justification for other ILM approaches. Systematic comparison is conducted for both in-domain and cross-domain evaluation on the Librispeech and TED-LIUM Release 2 corpora, respectively. Our proposed exact-ILM training can further improve the best ILM method.
Authors 4
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Affiliation as printed
RWTH Aachen University,Human Language Technology and Pattern Recognition, Computer Science Department,Aachen,Germany,52074
AppTek GmbH, Aachen, Germany
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Affiliation as printed
RWTH Aachen University,Human Language Technology and Pattern Recognition, Computer Science Department,Aachen,Germany,52074
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Affiliation as printed
RWTH Aachen University,Human Language Technology and Pattern Recognition, Computer Science Department,Aachen,Germany,52074
AppTek GmbH, Aachen, Germany
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Affiliation as printed
RWTH Aachen University,Human Language Technology and Pattern Recognition, Computer Science Department,Aachen,Germany,52074
AppTek GmbH, Aachen, Germany
Cited by 18 stored of 18
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Cited by patents worldwide 1 (Lens.org)
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