23 citations · 32 across the 5 of their papers we have counts for
6 papers
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition
Chao-Han Huck Yang, Zeeshan Ahmed, Yile Gu +5
In this work, we aim to enhance the system robustness of end-to-end automatic speech recognition (ASR) against adversarially-noisy speech examples. We focus on a rigorous and empir…
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…
Contextual Language Model Adaptation for Conversational Agents
Anirudh Raju, Behnam Hedayatnia, Linda Liu +5
Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents. These ASR systems should provide a high accuracy under…