Search Results for author:
Found 11 papers, 2 papers with code
Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network
In this study, we present a novel weighting prediction approach, which explicitly learns the task relationships from downstream training information to address the core challenge of universal speech enhancement.
Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search
Cascading multiple pre-trained models is an effective way to compose an end-to-end system.
GenDistiller: Distilling Pre-trained Language Models based on Generative Models
Self-supervised pre-trained models such as HuBERT and WavLM leverage unlabeled speech data for representation learning and offer significantly improve for numerous downstream tasks.
MFAS: Emotion Recognition through Multiple Perspectives Fusion Architecture Search Emulating Human Cognition
Additionally, considering that humans adjust their perception of emotional words in textual semantic based on certain cues present in speech, we design a novel search space and search for the optimal fusion strategy for the two types of information.
Meta Auxiliary Learning for Low-resource Spoken Language Understanding
Spoken language understanding (SLU) treats automatic speech recognition (ASR) and natural language understanding (NLU) as a unified task and usually suffers from data scarcity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A CTC Triggered Siamese Network with Spatial-Temporal Dropout for Speech Recognition
Analysis shows that our proposed approach brings a better uniformity for the trained model and enlarges the CTC spikes obviously.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multiple Confidence Gates For Joint Training Of SE And ASR
Joint training of speech enhancement model (SE) and speech recognition model (ASR) is a common solution for robust ASR in noisy environments.
Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE
Therefore, we previously proposed a harmonic gated compensation network (HGCN) to predict the full harmonic locations based on the unmasked harmonics and process the result of a coarse enhancement module to recover the masked harmonics.
HGCN: Harmonic gated compensation network for speech enhancement
Mask processing in the time-frequency (T-F) domain through the neural network has been one of the mainstreams for single-channel speech enhancement.
Identity-Enhanced Network for Facial Expression Recognition
Facial expression recognition is a challenging task, arguably because of large intra-class variations and high inter-class similarities.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Autoencoder Regularized Network For Driving Style Representation Learning
In this paper, we study learning generalized driving style representations from automobile GPS trip data.
