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Linda Liu

4 accepted papers

2023

Selective Structured State-Spaces for Long-Form Video Understanding

CVPR 2023poster

Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem. The recently proposed Structured State-Space Sequence (S4) model with its linear complexity offers a promising direction in this space. However, we demonstrate that treating all image-tokens equall…

Cited by 126SourcePDFScholar
2022

Mitigating Closed-Model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition

ICASSP 2022accepted

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 empirical "closed-model adversarial robustness" setting (e.g., on-device or cloud applications). The adversarial noise is onl…

Cited by 0SourceScholar
2021

Domain-Aware Neural Language Models for Speech Recognition

ICASSP 2021accepted

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-aware rescoring framework suitable for achieving domain-adaptation during second-pass rescoring in production settings.…

Cited by 21SourceScholar
2021

Personalization Strategies for End-to-End Speech Recognition Systems

ICASSP 2021accepted

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 second-pass rescoring strategies can be leveraged together to improve the recognition of such words. Following previous…

Cited by 0SourceScholar