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Found 22 papers, 3 papers with code
Streaming Anchor Loss: Augmenting Supervision with Temporal Significance
Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms.
Leveraging Large Language Models for Exploiting ASR Uncertainty
While large language models excel in a variety of natural language processing (NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they must either rely on off-the-shelf automatic speech recognition (ASR) systems for transcription, or be equipped with an in-built speech modality.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information
Automotive radar has increasingly attracted attention due to growing interest in autonomous driving technologies.
Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR
Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e. g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, we present a novel approach to predict the user's intent (the user speaking to the device or not) directly from acoustic and textual information encoded at subword tokens which are obtained via an end-to-end ASR model.
Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays
Using the learned self-attention of its image branch, RGT extracts a bounding box for which to compute radiomic features, which are further processed by the radiomics branch; learned image and radiomic features are then fused and mutually interact via cross-attention layers.
Improving Voice Trigger Detection with Metric Learning
A detector is typically trained on speech data independent of speaker information and used for the voice trigger detection task.
Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models
We also show that the ensemble of the LatticeRNN and acoustic-distilled models brings further accuracy improvement of 20%.
End-to-end system for object detection from sub-sampled radar data
We show robust detection based on radar data reconstructed using 20% of samples under extreme weather conditions such as snow or fog, and on low-illuminated nights.
CheXT: Knowledge-Guided Cross-Attention Transformer for Abnormality Classification and Localization in Chest X-rays
During training, the image branch leverages its learned attention to estimate pathology localization, which is then utilized to extract radiomic features from images in the radiomics branch.
Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop
The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation.
Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.
Automotive Radar Data Acquisition using Object Detection
In this paper, we introduce an algorithm to utilize object detection results from the image to adaptively sample and acquire radar data using Compressed Sensing (CS).
Adaptive Automotive Radar data Acquisition
We use previous radar frame information to mitigate the potential information loss of an object missed by the image or the object detection network.
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems
In this paper we introduce a recurrent neural network (RNN) based variational autoencoder (VAE) model with a new constrained loss function that can generate more meaningful electroencephalography (EEG) features from raw EEG features to improve the performance of EEG based speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Understanding effect of speech perception in EEG based speech recognition systems
The electroencephalography (EEG) signals recorded in parallel with speech are used to perform isolated and continuous speech recognition.
Improving EEG based continuous speech recognition using GAN
In this paper we demonstrate that it is possible to generate more meaningful electroencephalography (EEG) features from raw EEG features using generative adversarial networks (GAN) to improve the performance of EEG based continuous speech recognition systems.
Predicting Different Acoustic Features from EEG and towards direct synthesis of Audio Waveform from EEG
In [1, 2] authors provided preliminary results for synthesizing speech from electroencephalography (EEG) features where they first predict acoustic features from EEG features and then the speech is reconstructed from the predicted acoustic features using griffin lim reconstruction algorithm.
Predicting Video features from EEG and Vice versa
In this paper we explore predicting facial or lip video features from electroencephalography (EEG) features and predicting EEG features from recorded facial or lip video frames using deep learning models.
Advancing Speech Synthesis using EEG
In this paper we introduce attention-regression model to demonstrate predicting acoustic features from electroencephalography (EEG) features recorded in parallel with spoken sentences.
Speaker Identification using EEG
In this paper we explore speaker identification using electroencephalography (EEG) signals.
Detecting Patch Adversarial Attacks with Image Residuals
We introduce an adversarial sample detection algorithm based on image residuals, specifically designed to guard against patch-based attacks.
Continuous Silent Speech Recognition using EEG
Our results demonstrate the feasibility of using EEG signals for performing continuous silent speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
