[PDF][PDF] Recent advances in end-to-end automatic speech recognition
J Li - APSIPA Transactions on Signal and Information …, 2022 - nowpublishers.com
Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …
Enabling resource-efficient aiot system with cross-level optimization: A survey
The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …
widespread use of intelligent infrastructures and the impressive success of deep learning …
Structured pruning of large language models
Z Wang, J Wohlwend, T Lei - ar** Recurrent Neural
Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications …
Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications …
Automatic speech recognition using limited vocabulary: A survey
JLKE Fendji, DCM Tala, BO Yenke… - Applied Artificial …, 2022 - Taylor & Francis
ABSTRACT Automatic Speech Recognition (ASR) is an active field of research due to its
large number of applications and the proliferation of interfaces or computing devices that …
large number of applications and the proliferation of interfaces or computing devices that …
CHIMERA: A 0.92-TOPS, 2.2-TOPS/W edge AI accelerator with 2-MByte on-chip foundry resistive RAM for efficient training and inference
Implementing edge artificial intelligence (AI) inference and training is challenging with
current memory technologies. As deep neural networks (DNNs) grow in size, this problem is …
current memory technologies. As deep neural networks (DNNs) grow in size, this problem is …