168 citations · 743 across the 44 of their papers we have counts for
6 papers · 1 filter
When CTC Training Meets Acoustic Landmarks
Di He, Xuesong Yang, Boon Pang Lim +3
Connectionist temporal classification (CTC) provides an end-to-end acoustic model (AM) training strategy. CTC learns accurate AMs without time-aligned phonetic transcription, but s…
Design Flow of Accelerating Hybrid Extremely Low Bit-width Neural Network in Embedded FPGA
Junsong Wang, Qiuwen Lou, Xiaofan Zhang +3
Neural network accelerators with low latency and low energy consumption are desirable for edge computing. To create such accelerators, we propose a design flow for accelerating the…
Improved ASR for Under-Resourced Languages Through Multi-Task Learning with Acoustic Landmarks
Di He, Boon Pang Lim, Xuesong Yang +2
Furui first demonstrated that the identity of both consonant and vowel can be perceived from the C-V transition; later, Stevens proposed that acoustic landmarks are the primary cue…
Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs
Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang +3
Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks. However, deploying deep CNN models for latency-critical tasks remain…
ASAP: Accelerated Short-Read Alignment on Programmable Hardware
Subho S. Banerjee, Mohamed El-Hadedy, Jong Bin Lim +4
The proliferation of high-throughput sequencing machines ensures rapid generation of up to billions of short nucleotide fragments in a short period of time. This massive amount of…
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
Yuhong Li, Xiaofan Zhang, Deming Chen
We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurat…