Papers by Lyan Verwimp
Application-Agnostic Language Modeling for On-Device ASR (2023.acl-industry)
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| Challenge: | On-device automatic speech recognition systems face several challenges compared to server-based systems. |
| Approach: | They propose to use a feed-forward language model to build a single application-agnostic model . they propose to reduce disk size by half while maintaining speed and accuracy of original model a . |
| Outcome: | The proposed architecture reduces disk size by half while maintaining speed and accuracy of the original model. |
TF-LM: TensorFlow-based Language Modeling Toolkit (L18-1)
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| Challenge: | Existing deep learning tools offer building blocks but training and building models takes time and knowledge. |
| Approach: | They propose to make available LSTM language models trained on Dutch texts and English benchmarks. |
| Outcome: | The proposed model can be used to test the perplexity, predict the next word(s), re-score hypotheses or generate debugging files for interpolation with n-gram models. |