no code implementations • 17 Oct 2022 • Kai Zhen, Martin Radfar, Hieu Duy Nguyen, Grant P. Strimel, Nathan Susanj, Athanasios Mouchtaris
For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 30 Jun 2022 • Kai Zhen, Hieu Duy Nguyen, Raviteja Chinta, Nathan Susanj, Athanasios Mouchtaris, Tariq Afzal, Ariya Rastrow
We present a novel sub-8-bit quantization-aware training (S8BQAT) scheme for 8-bit neural network accelerators.
no code implementations • 27 Mar 2021 • Kai Zhen, Jongmo Sung, Mi Suk Lee, Seungkwon Beak, Minje Kim
We formulate the speech coding problem as an autoencoding task, where a convolutional neural network (CNN) performs encoding and decoding as a neural waveform codec (NWC) during its feedforward routine.
no code implementations • 9 Feb 2021 • Kai Zhen, Hieu Duy Nguyen, Feng-Ju Chang, Athanasios Mouchtaris, Ariya Rastrow, .
In the literature, such methods are referred to as sparse pruning.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 31 Dec 2020 • Kai Zhen, Mi Suk Lee, Jongmo Sung, SeungKwon Beack, Minje Kim
Conventional audio coding technologies commonly leverage human perception of sound, or psychoacoustics, to reduce the bitrate while preserving the perceptual quality of the decoded audio signals.
no code implementations • 18 Aug 2019 • Kai Zhen, Mi Suk Lee, Minje Kim
In speech enhancement, an end-to-end deep neural network converts a noisy speech signal to a clean speech directly in time domain without time-frequency transformation or mask estimation.
no code implementations • 18 Jun 2019 • Kai Zhen, Jongmo Sung, Mi Suk Lee, Seung-Kwon Beack, Minje Kim
Speech codecs learn compact representations of speech signals to facilitate data transmission.
no code implementations • 15 Mar 2017 • Kai Zhen, Mridul Birla, David Crandall, Bingjing Zhang, Judy Qiu
Given the progress in image recognition with recent data driven paradigms, it's still expensive to manually label a large training data to fit a convolutional neural network (CNN) model.