1 code implementation • 5 Jul 2023 • Jakob Drachmann Havtorn, Amelie Royer, Tijmen Blankevoort, Babak Ehteshami Bejnordi
The input tokens to Vision Transformers carry little semantic meaning as they are defined as regular equal-sized patches of the input image, regardless of its content.
no code implementations • 1 Mar 2022 • Lasse Borgholt, Jakob Drachmann Havtorn, Joakim Edin, Lars Maaløe, Christian Igel
Unsupervised representation learning for speech processing has matured greatly in the last few years.
no code implementations • 29 Nov 2021 • Lasse Borgholt, Jakob Drachmann Havtorn, Mostafa Abdou, Joakim Edin, Lars Maaløe, Anders Søgaard, Christian Igel
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
no code implementations • 29 Sep 2021 • Jakob Drachmann Havtorn, Lasse Borgholt, Jes Frellsen, Søren Hauberg, Lars Maaløe
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.
no code implementations • 17 Feb 2021 • Lasse Borgholt, Jakob Drachmann Havtorn, Željko Agić, Anders Søgaard, Lars Maaløe, Christian Igel
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.
no code implementations • 1 Feb 2021 • Lasse Borgholt, Tycho Max Sylvester Tax, Jakob Drachmann Havtorn, Lars Maaløe, Christian Igel
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.