Search Results for author:
Found 11 papers, 2 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
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.
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
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
