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Found 18 papers, 5 papers with code
Predicting Generalization of AI Colonoscopy Models to Unseen Data
We test MSN's ability to be trained on data only from Israel and detect unseen techniques, narrow-band imaging (NBI) and chromendoscoy (CE), on colonoscopes from Japan (354 videos, 128 hours).
The unreasonable effectiveness of AI CADe polyp detectors to generalize to new countries
$\textbf{Conclusion}$: Differences that prevent CADe detectors from performing well in non-medical settings do not degrade the performance of our AI CADe polyp detector when applied to data from a new country.
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings
We demonstrate that this metric more closely aligns with clinician preferences on medical sentences as compared to other metrics (WER, BLUE, METEOR, etc), sometimes by wide margins.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Knowledge distillation for fast and accurate DNA sequence correction
Accurate genome sequencing can improve our understanding of biology and the genetic basis of disease.
The Need for Medically Aware Video Compression in Gastroenterology
Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored.
TRILLsson: Distilled Universal Paralinguistic Speech Representations
Our largest distilled model is less than 15% the size of the original model (314MB vs 2. 2GB), achieves over 96% the accuracy on 6 of 7 tasks, and is trained on 6. 5% the data.
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers
Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishing real from synthetic speech.
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio.
Ranked #1 on
Speech Recognition
on Common Voice
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment.
Towards Learning a Universal Non-Semantic Representation of Speech
The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a pre-existing embedding model trained for different datasets or tasks.
Personalizing ASR for Dysarthric and Accented Speech with Limited Data
In this paper, we present and evaluate finetuning techniques to improve ASR for users with non-standard speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody.
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.
Spatially adaptive image compression using a tiled deep network
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images.
Uncovering Latent Style Factors for Expressive Speech Synthesis
Prosodic modeling is a core problem in speech synthesis.
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Our methods introduce a multi-pass training method to combine the training goals of high-quality reconstructions in areas around stop-code masking as well as in highly-detailed areas.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM.
Full Resolution Image Compression with Recurrent Neural Networks
As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
