137 citations · 350 across the 20 of their papers we have counts for
10 papers · 1 filter
Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study
W. Ronny Huang, Cyril Allauzen, Tongzhou Chen +7
In the era of large models, the autoregressive nature of decoding often results in latency serving as a significant bottleneck. We propose a non-autoregressive LM-fused ASR system…
Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR
W. Ronny Huang, Hao Zhang, Shankar Kumar +2
We propose a method of segmenting long-form speech by separating semantically complete sentences within the utterance. This prevents the ASR decoder from needlessly processing fara…
E2E Segmentation in a Two-Pass Cascaded Encoder ASR Model
W. Ronny Huang, Shuo-Yiin Chang, Tara N. Sainath +7
We explore unifying a neural segmenter with two-pass cascaded encoder ASR into a single model. A key challenge is allowing the segmenter (which runs in real-time, synchronously wit…
Modular Hybrid Autoregressive Transducer
Zhong Meng, Tongzhou Chen, Rohit Prabhavalkar +10
Text-only adaptation of a transducer model remains challenging for end-to-end speech recognition since the transducer has no clearly separated acoustic model (AM), language model (…
Detecting Unintended Memorization in Language-Model-Fused ASR
W. Ronny Huang, Steve Chien, Om Thakkar +1
End-to-end (E2E) models are often being accompanied by language models (LMs) via shallow fusion for boosting their overall quality as well as recognition of rare words. At the same…
Improving Rare Word Recognition with LM-aware MWER Training
Weiran Wang, Tongzhou Chen, Tara N. Sainath +10
Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the…
Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition
W. Ronny Huang, Cal Peyser, Tara N. Sainath +3
Language model fusion helps smart assistants recognize words which are rare in acoustic data but abundant in text-only corpora (typed search logs). However, such corpora have prope…
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model
Hao Zhang, You-Chi Cheng, Shankar Kumar +3
Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text. We propose a fast, accurate and compact two-level hierar…
Scaling End-to-End Models for Large-Scale Multilingual ASR
Bo Li, Ruoming Pang, Tara N. Sainath +7
Building ASR models across many languages is a challenging multi-task learning problem due to large variations and heavily unbalanced data. Existing work has shown positive transfe…
Lookup-Table Recurrent Language Models for Long Tail Speech Recognition
W. Ronny Huang, Tara N. Sainath, Cal Peyser +3
We introduce Lookup-Table Language Models (LookupLM), a method for scaling up the size of RNN language models with only a constant increase in the floating point operations, by inc…