12 citations · 21 across the 3 of their papers we have counts for
7 papers
Domain-aware Neural Language Models for Speech Recognition
Linda Liu, Yile Gu, Aditya Gourav +5
As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains. We present a domain-…
Personalization Strategies for End-to-End Speech Recognition Systems
Aditya Gourav, Linda Liu, Ankur Gandhe +9
The recognition of personalized content, such as contact names, remains a challenging problem for end-to-end speech recognition systems. In this work, we demonstrate how first and…
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion
Vijay Ravi, Yile Gu, Ankur Gandhe +5
End-to-end automatic speech recognition (ASR) systems, such as recurrent neural network transducer (RNN-T), have become popular, but rare word remains a challenge. In this paper, w…
Multi-task Language Modeling for Improving Speech Recognition of Rare Words
Chao-Han Huck Yang, Linda Liu, Ankur Gandhe +4
End-to-end automatic speech recognition (ASR) systems are increasingly popular due to their relative architectural simplicity and competitive performance. However, even though the…
Neural Composition: Learning to Generate from Multiple Models
Denis Filimonov, Ravi Teja Gadde, Ariya Rastrow
Decomposing models into multiple components is critically important in many applications such as language modeling (LM) as it enables adapting individual components separately and…
Neural Machine Translation For Paraphrase Generation
Alex Sokolov, Denis Filimonov
Training a spoken language understanding system, as the one in Alexa, typically requires a large human-annotated corpus of data. Manual annotations are expensive and time consuming…