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Found 8 papers, 1 papers with code
Broadcasted Residual Learning for Efficient Keyword Spotting
We present a broadcasted residual learning method to achieve high accuracy with small model size and computational load.
Ranked #2 on
Keyword Spotting
on Google Speech Commands
Orthogonality Constrained Multi-Head Attention For Keyword Spotting
Augmenting regularization terms which penalize positional and contextual non-orthogonality between the attention heads encourages to output different representations from separate subsequences, which in turn enables leveraging structured information without explicit sequence models such as hidden Markov models.
Query-by-example on-device keyword spotting
A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles
Existing inter-vehicle distance estimation methods assume that the ego and target vehicles drive on a same ground plane.
Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames
Second, conventional monocular SLAM uses inappropriate mapping factors such as dynamic objects and low-parallax areas in mapping.
RT-MOT: Confidence-Aware Real-Time Scheduling Framework for Multi-Object Tracking Tasks
Focusing on multiple choices of a workload pair of detection and association, which are two main components of the tracking-by-detection approach for MOT, we tailor a measure of object confidence for RT-MOT and develop how to estimate the measure for the next frame of each MOT task.
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data
Vehicle control data is constructed by many hours of human driving, and it is challenging to construct large vehicle control datasets.
Knowledge Distillation from Non-streaming to Streaming ASR Encoder using Auxiliary Non-streaming Layer
Streaming automatic speech recognition (ASR) models are restricted from accessing future context, which results in worse performance compared to the non-streaming models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
