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Found 11 papers, 7 papers with code
A comparison study on patient-psychologist voice diarization
Conversations between a clinician and a patient, in natural conditions, are valuable sources of information for medical follow-up.
Introducing topography in convolutional neural networks
Thus, in this work, inspired by the neuroscience literature, we proposed a new topographic inductive bias in Convolutional Neural Networks (CNNs).
Learning strides in convolutional neural networks
In particular, we show that introducing our layer into a ResNet-18 architecture allows keeping consistent high performance on CIFAR10, CIFAR100 and ImageNet even when training starts from poor random stride configurations.
Learning spectro-temporal representations of complex sounds with parameterized neural networks
We found out that models based on Learnable STRFs are on par for all tasks with different toplines, and obtain the best performance for Speech Activity Detection.
Comparison of Speaker Role Recognition and Speaker Enrollment Protocol for conversational Clinical Interviews
Here, we proposed a split of the data that allows conducting a comparative evaluation of speaker role recognition and speaker enrollment methods to solve this task.
Vocal markers from sustained phonation in Huntington's Disease
According to our regression results, Phonatory features are suitable for the predictions of clinical performance in Huntington's Disease.
Seshat: A tool for managing and verifying annotation campaigns of audio data
We introduce Seshat, a new, simple and open-source software to efficiently manage annotations of speech corpora.
Identification of primary and collateral tracks in stuttered speech
Here, we introduce a new evaluation framework for disfluency detection inspired by the clinical and NLP perspective together with the theory of performance from \cite{clark1996using} which distinguishes between primary and collateral tracks.
Sampling strategies in Siamese Networks for unsupervised speech representation learning
We apply these results to pairs of words discovered using an unsupervised algorithm and show an improvement on state-of-the-art in unsupervised representation learning using siamese networks.
XNMT: The eXtensible Neural Machine Translation Toolkit
In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing.
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography.
