Search Results for author: Jaejun Lee

Found 7 papers, 4 papers with code

Howl: A Deployed, Open-Source Wake Word Detection System

2 code implementations EMNLP (NLPOSS) 2020 Raphael Tang, Jaejun Lee, Afsaneh Razi, Julia Cambre, Ian Bicking, Jofish Kaye, Jimmy Lin

We describe Howl, an open-source wake word detection toolkit with native support for open speech datasets, like Mozilla Common Voice and Google Speech Commands.

Keyword Spotting

Showing Your Work Doesn't Always Work

1 code implementation ACL 2020 Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yao-Liang Yu, Jimmy Lin

In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks.

DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

3 code implementations ACL 2020 Ji Xin, Raphael Tang, Jaejun Lee, Yao-Liang Yu, Jimmy Lin

Large-scale pre-trained language models such as BERT have brought significant improvements to NLP applications.

What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning

no code implementations8 Nov 2019 Jaejun Lee, Raphael Tang, Jimmy Lin

We show that only a fourth of the final layers need to be fine-tuned to achieve 90% of the original quality.

Linguistic Acceptability Natural Language Inference +4

Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting

no code implementations IJCNLP 2019 Jaejun Lee, Raphael Tang, Jimmy Lin

Used for simple commands recognition on devices from smart speakers to mobile phones, keyword spotting systems are everywhere.

Keyword Spotting

JavaScript Convolutional Neural Networks for Keyword Spotting in the Browser: An Experimental Analysis

1 code implementation30 Oct 2018 Jaejun Lee, Raphael Tang, Jimmy Lin

Overall, our robust, cross-device implementation for keyword spotting realizes a new paradigm for serving neural network applications, and one of our slim models reduces latency by 66% with a minimal decrease in accuracy of 4% from 94% to 90%.

Keyword Spotting Model Compression

Fuzzy and entropy facial recognition

no code implementations24 Aug 2014 Jaejun Lee, Taeseon Yun

This paper suggests an effective method for facial recognition using fuzzy theory and Shannon entropy.

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