Search Results for author:
Found 20 papers, 8 papers with code
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
Medical coding is the task of assigning medical codes to clinical free-text documentation.
Ranked #1 on
Medical Code Prediction
on MIMIC-IV ICD-10
Self-Supervised Speech Representation Learning: A Review
Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Model-agnostic out-of-distribution detection using combined statistical tests
These techniques, based on classical statistical tests, are model-agnostic in the sense that they can be applied to any differentiable generative model.
A Brief Overview of Unsupervised Neural Speech Representation Learning
Unsupervised representation learning for speech processing has matured greatly in the last few years.
Benchmarking Generative Latent Variable Models for Speech
Stochastic latent variable models (LVMs) achieve state-of-the-art performance on natural image generation but are still inferior to deterministic models on speech.
Do We Still Need Automatic Speech Recognition for Spoken Language Understanding?
We compare learned speech features from wav2vec 2. 0, state-of-the-art ASR transcripts, and the ground truth text as input for a novel speech-based named entity recognition task, a cardiac arrest detection task on real-world emergency calls and two existing SLU benchmarks.
Ranked #7 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
Towards Generative Latent Variable Models for Speech
While stochastic latent variable models (LVMs) now achieve state-of-the-art performance on natural image generation, they are still inferior to deterministic models on speech.
Do End-to-End Speech Recognition Models Care About Context?
We test this hypothesis by measuring temporal context sensitivity and evaluate how the models perform when we constrain the amount of contextual information in the audio input.
Hierarchical VAEs Know What They Don't Know
Deep generative models have been demonstrated as state-of-the-art density estimators.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
On Scaling Contrastive Representations for Low-Resource Speech Recognition
We explore the performance of such systems without fine-tuning by training a state-of-the-art speech recognizer on the fixed representations from the computationally demanding wav2vec 2. 0 framework.
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech
We address a challenging and practical task of labeling questions in speech in real time during telephone calls to emergency medical services in English, which embeds within a broader decision support system for emergency call-takers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Towards Hierarchical Discrete Variational Autoencoders
We introduce the Hierarchical Discrete Variational Autoencoder (HD-VAE): a hi- erarchy of variational memory layers.
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
In this paper we close the performance gap by constructing VAE models that can effectively utilize a deep hierarchy of stochastic variables and model complex covariance structures.
Ranked #18 on
Image Generation
on ImageNet 32x32
(bpd metric)
Feature Map Variational Auto-Encoders
There have been multiple attempts with variational auto-encoders (VAE) to learn powerful global representations of complex data using a combination of latent stochastic variables and an autoregressive model over the dimensions of the data.
Utilizing Domain Knowledge in End-to-End Audio Processing
End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations.
Environmental Sound Classification
General Classification
+1
Exploiting Nontrivial Connectivity for Automatic Speech Recognition
Nontrivial connectivity has allowed the training of very deep networks by addressing the problem of vanishing gradients and offering a more efficient method of reusing parameters.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Semi-Supervised Generation with Cluster-aware Generative Models
Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning.
Auxiliary Deep Generative Models
The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive.
Ranked #49 on
Image Classification
on SVHN
Ladder Variational Autoencoders
Variational Autoencoders are powerful models for unsupervised learning.
Recurrent Spatial Transformer Networks
We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input images without deteriorating performance.
