64 citations · 99 across the 12 of their papers we have counts for
12 papers
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…
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes
Shaojin Ding, Weiran Wang, Ding Zhao +11
In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios. Moreover, the model can sign…
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition
Shaojin Ding, Rajeev Rikhye, Qiao Liang +5
Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile d…
4-bit Conformer with Native Quantization Aware Training for Speech Recognition
Shaojin Ding, Phoenix Meadowlark, Yanzhang He +3
Reducing the latency and model size has always been a significant research problem for live Automatic Speech Recognition (ASR) application scenarios. Along this direction, model qu…
Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis
Mu Yang, Shaojin Ding, Tianlong Chen +2
This work presents a lifelong learning approach to train a multilingual Text-To-Speech (TTS) system, where each language was seen as an individual task and was learned sequentially…
Textual Echo Cancellation
Shaojin Ding, Ye Jia, Ke Hu +1
In this paper, we propose Textual Echo Cancellation (TEC) - a framework for cancelling the text-to-speech (TTS) playback echo from overlapping speech recordings. Such a system can…
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…
ABD-Net: Attentive but Diverse Person Re-Identification
Tianlong Chen, Shaojin Ding, Jingyi Xie +5
Attention mechanism has been shown to be effective for person re-identification (Re-ID). However, the learned attentive feature embeddings which are often not naturally diverse nor…
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…