14 citations · 14 across the 2 of their papers we have counts for
6 papers · 1 filter
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models
Shaojin Ding, David Qiu, David Rim +10
End-to-end automatic speech recognition (ASR) models have seen revolutionary quality gains with the recent development of large-scale universal speech models (USM). However, deploy…
2-bit Conformer quantization for automatic speech recognition
Oleg Rybakov, Phoenix Meadowlark, Shaojin Ding +4
Large speech models are rapidly gaining traction in research community. As a result, model compression has become an important topic, so that these models can fit in memory and be…
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models
David Qiu, David Rim, Shaojin Ding +2
With the rapid increase in the size of neural networks, model compression has become an important area of research. Quantization is an effective technique at decreasing the model s…
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models
Steven M. Hernandez, Ding Zhao, Shaojin Ding +5
Continued improvements in machine learning techniques offer exciting new opportunities through the use of larger models and larger training datasets. However, there is a growing ne…
AutoSpeech: Neural Architecture Search for Speaker Recognition
Shaojin Ding, Tianlong Chen, Xinyu Gong +2
Speaker recognition systems based on Convolutional Neural Networks (CNNs) are often built with off-the-shelf backbones such as VGG-Net or ResNet. However, these backbones were orig…
Personal VAD: Speaker-Conditioned Voice Activity Detection
Shaojin Ding, Quan Wang, Shuo-yiin Chang +2
In this paper, we propose "personal VAD", a system to detect the voice activity of a target speaker at the frame level. This system is useful for gating the inputs to a streaming o…