371 citations · 448 across the 12 of their papers we have counts for
13 papers
Symphony for Speech-to-Text: Supporting Real-Time Medical Voice Interfaces
Arne Nix, Robert James, Lasse Borgholt +6
After decades of use in dictation and, more recently, ambient documentation, speech is emerging as a primary modality for interacting with technology and AI in healthcare. Yet medi…
A Text-To-Text Alignment Algorithm for Better Evaluation of Modern Speech Recognition Systems
Lasse Borgholt, Jakob Havtorn, Christian Igel +2
Modern neural networks have greatly improved performance across speech recognition benchmarks. However, gains are often driven by frequent words with limited semantic weight, which…
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records
Joakim Edin, Maria Maistro, Lars Maaløe +3
Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantl…
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
Joakim Edin, Alexander Junge, Jakob D. Havtorn +4
Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatm…
Self-Supervised Speech Representation Learning: A Review
Abdelrahman Mohamed, Hung-yi Lee, Lasse Borgholt +9
Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenari…
A Brief Overview of Unsupervised Neural Speech Representation Learning
Lasse Borgholt, Jakob Drachmann Havtorn, Joakim Edin +2
Unsupervised representation learning for speech processing has matured greatly in the last few years. Work in computer vision and natural language processing has paved the way, but…
Benchmarking Generative Latent Variable Models for Speech
Jakob D. Havtorn, Lasse Borgholt, Søren Hauberg +2
Stochastic latent variable models (LVMs) achieve state-of-the-art performance on natural image generation but are still inferior to deterministic models on speech. In this paper, w…
Do We Still Need Automatic Speech Recognition for Spoken Language Understanding?
Lasse Borgholt, Jakob Drachmann Havtorn, Mostafa Abdou +4
Spoken language understanding (SLU) tasks are usually solved by first transcribing an utterance with automatic speech recognition (ASR) and then feeding the output to a text-based…
Do End-to-End Speech Recognition Models Care About Context?
Lasse Borgholt, Jakob Drachmann Havtorn, Željko Agić +3
The two most common paradigms for end-to-end speech recognition are connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. It has been argued…
On Scaling Contrastive Representations for Low-Resource Speech Recognition
Lasse Borgholt, Tycho Max Sylvester Tax, Jakob Drachmann Havtorn +2
Recent advances in self-supervised learning through contrastive training have shown that it is possible to learn a competitive speech recognition system with as little as 10 minute…
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech
Jakob D. Havtorn, Jan Latko, Joakim Edin +6
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 broa…
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition
Jan Kremer, Lasse Borgholt, Lars Maaløe
End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (W…
Exploiting Nontrivial Connectivity for Automatic Speech Recognition
Marius Paraschiv, Lasse Borgholt, Tycho Max Sylvester Tax +2
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.…