no code implementations • 25 Apr 2024 • Chanho Park, Mingjie Chen, Thomas Hain
In WER estimation experiments, the proposed method reaches a similar performance to ASR system-dependent WER estimators on in-domain data and achieves state-of-the-art performance on out-of-domain data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 25 Mar 2024 • Chanho Park, H. Vincent Poor, Namyoon Lee
SignSGD with majority voting (signSGD-MV) is an effective distributed learning algorithm that can significantly reduce communication costs by one-bit quantization.
no code implementations • 2 Feb 2024 • Chanho Park, Namyoon Lee
Distributed learning is an effective approach to accelerate model training using multiple workers.
no code implementations • 23 Nov 2023 • Juil Koo, Chanho Park, Minhyuk Sung
PDS matches the stochastic latents of the source and the target, enabling the sampling of targets in diverse parameter spaces that align with a desired attribute while maintaining the source's identity.
no code implementations • 12 Oct 2023 • Chanho Park, Chengsong Lu, Mingjie Chen, Thomas Hain
WER estimation is a task aiming to predict the WER of an ASR system, given a speech utterance and a transcription.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 15 Feb 2023 • Chanho Park, Namyoon Lee
The training efficiency of complex deep learning models can be significantly improved through the use of distributed optimization.
no code implementations • 25 Jul 2022 • Chanho Park, Rehan Ahmad, Thomas Hain
By using the submodular function, a training set for automatic speech recognition matching the target data set is selected.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 14 Sep 2021 • Chanho Park, Seunghoon Lee, Namyoon Lee
In this paper, we present a simple yet effective precoding method with limited channel knowledge, called sign-alignment precoding.
no code implementations • 31 Dec 2020 • Seunghoon Lee, Chanho Park, Song-Nam Hong, Yonina C. Eldar, Namyoon Lee
This paper proposes a Bayesian federated learning (BFL) algorithm to aggregate the heterogeneous quantized gradient information optimally in the sense of minimizing the mean-squared error (MSE).