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Michael Levit
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Publications
- 2023
- [i1]Michael Levit, Sarangarajan Parthasarathy, Cem Aksoylar, Mohammad Sadegh Rasooli, Shuangyu Chang:
External Language Model Integration for Factorized Neural Transducers. CoRR abs/2305.17304 (2023) - 2018
- [c24]Michael Levit, Sarangarajan Parthasarathy, Shuangyu Chang:
What to Expect from Expected Kneser-Ney Smoothing. INTERSPEECH 2018: 3378-3382 - 2017
- [c23]Michael Levit, Yan Huang, Shuangyu Chang, Yifan Gong:
Don't Count on ASR to Transcribe for You: Breaking Bias with Two Crowds. INTERSPEECH 2017: 3941-3945 - 2016
- [c22]Michael Levit, Sarangarajan Parthasarathy, Shuangyu Chang:
Word-Phrase-Entity Recurrent Neural Networks for Language Modeling. INTERSPEECH 2016: 3514-3518 - 2015
- [c21]Shuangyu Chang, Abhik Lahiri, Issac Alphonso, Barlas Oguz, Michael Levit, Benoît Dumoulin:
Discriminative training of context-dependent language model scaling factors and interpolation weights. ASRU 2015: 45-51 - [c20]Michael Levit, Andreas Stolcke, Shuangyu Chang, Sarangarajan Parthasarathy:
Token-level interpolation for class-based language models. ICASSP 2015: 5426-5430 - [c19]Michael Levit, Andreas Stolcke, R. Subba, Sarangarajan Parthasarathy, Shuangyu Chang, S. Xie, T. Anastasakos, Benoît Dumoulin:
Personalization of word-phrase-entity language models. INTERSPEECH 2015: 448-452 - 2014
- [c18]Michael Levit, Sarangarajan Parthasarathy, Shuangyu Chang, Andreas Stolcke, Benoît Dumoulin:
Word-phrase-entity language models: getting more mileage out of n-grams. INTERSPEECH 2014: 666-670 - 2013
- [c16]Shuangyu Chang, Michael Levit, Partha Parthasarathy, Benoît Dumoulin:
Improving unsupervised language model adaptation with discriminative data filtering. INTERSPEECH 2013: 1208-1212 - 2012
- [c15]Michael Levit, Shuangyu Chang, Bruce Buntschuh, Nick Kibre:
End-to-end speech recognition accuracy metric for voice-search tasks. ICASSP 2012: 5141-5144 - 2009
- [c13]Michael Levit, Shuangyu Chang, Bruce Buntschuh:
Garbage modeling with decoys for a sequential recognition scenario. ASRU 2009: 468-473
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