12 citations · 12 across the 4 of their papers we have counts for
5 papers
HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding
Hanbin Bae, Heejin Choi, Joohyung Lee +3
Device-cloud speculative decoding reduces server computation, but growing a deep draft tree with a small language model (SLM) requires repeated passes on a mobile neural processing…
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning
Joonyoung Kim, Kangwook Lee, Haebin Shin +5
The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short, and there are just too…
Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge
Sangjun Park, Kihyun Choo, Joohyung Lee +3
Text-to-Speech (TTS) services that run on edge devices have many advantages compared to cloud TTS, e.g., latency and privacy issues. However, neural vocoders with a low complexity…
Dynamic Noise Embedding: Noise Aware Training and Adaptation for Speech Enhancement
Joohyung Lee, Youngmoon Jung, Myunghun Jung +1
Estimating noise information exactly is crucial for noise aware training in speech applications including speech enhancement (SE) which is our focus in this paper. To estimate nois…
Dual Attention in Time and Frequency Domain for Voice Activity Detection
Joohyung Lee, Youngmoon Jung, Hoirin Kim
Voice activity detection (VAD) is a challenging task in low signal-to-noise ratio (SNR) environment, especially in non-stationary noise. To deal with this issue, we propose a novel…