Search Results for author: Linda Liu

Found 10 papers, 1 papers with code

Multi-task Language Modeling for Improving Speech Recognition of Rare Words

no code implementations23 Nov 2020 Chao-Han Huck Yang, Linda Liu, Ankur Gandhe, Yile Gu, Anirudh Raju, Denis Filimonov, Ivan Bulyko

We show that our rescoring model trained with these additional tasks outperforms the baseline rescoring model, trained with only the language modeling task, by 1. 4% on a general test and by 2. 6% on a rare word test set in terms of word-error-rate relative (WERR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion

no code implementations30 Nov 2020 Vijay Ravi, Yile Gu, Ankur Gandhe, Ariya Rastrow, Linda Liu, Denis Filimonov, Scott Novotney, Ivan Bulyko

We show that this simple method can improve performance on rare words by 3. 7% WER relative without degradation on general test set, and the improvement from USF is additive to any additional language model based rescoring.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Domain-aware Neural Language Models for Speech Recognition

no code implementations5 Jan 2021 Linda Liu, Yile Gu, Aditya Gourav, Ankur Gandhe, Shashank Kalmane, Denis Filimonov, Ariya Rastrow, Ivan Bulyko

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.

Domain Adaptation domain classification +3

Personalization Strategies for End-to-End Speech Recognition Systems

no code implementations15 Feb 2021 Aditya Gourav, Linda Liu, Ankur Gandhe, Yile Gu, Guitang Lan, Xiangyang Huang, Shashank Kalmane, Gautam Tiwari, Denis Filimonov, Ariya Rastrow, Andreas Stolcke, Ivan Bulyko

We also describe a novel second-pass de-biasing approach: used in conjunction with a first-pass shallow fusion that optimizes on oracle WER, we can achieve an additional 14% improvement on personalized content recognition, and even improve accuracy for the general use case by up to 2. 5%.

speech-recognition Speech Recognition

AVT: Audio-Video Transformer for Multimodal Action Recognition

no code implementations Submitted to ICLR 2022 Wentao Zhu, Jingru Yi, Kevin Hsu, Xiaohang Sun, Xiang Hao, Linda Liu, Mohamed Omar

AVT uses a combination of video and audio signals to improve action recognition accuracy, leveraging the effective spatio-temporal representation by the video Transformer.

Action Recognition Audio Classification +3

Selective Structured State-Spaces for Long-Form Video Understanding

no code implementations CVPR 2023 Jue Wang, Wentao Zhu, Pichao Wang, Xiang Yu, Linda Liu, Mohamed Omar, Raffay Hamid

To address this limitation, we present a novel Selective S4 (i. e., S5) model that employs a lightweight mask generator to adaptively select informative image tokens resulting in more efficient and accurate modeling of long-term spatiotemporal dependencies in videos.

Contrastive Learning Video Understanding

Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based Vision and Language Models

1 code implementation5 Nov 2023 Jingru Yi, Burak Uzkent, Oana Ignat, Zili Li, Amanmeet Garg, Xiang Yu, Linda Liu

While we demonstrate our data augmentation method with MDETR framework, the proposed approach is applicable to common grounding-based vision and language tasks with other frameworks.

Data Augmentation Phrase Grounding +1

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