Search Results for author:
Found 12 papers, 6 papers with code
Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO
Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted structures derived from atom-atom pair potentials in disordered materials, has been a longstanding challenge in condensed matter physics.
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators.
Acoustic-to-articulatory Speech Inversion with Multi-task Learning
Multi-task learning (MTL) frameworks have proven to be effective in diverse speech related tasks like automatic speech recognition (ASR) and speech emotion recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data.
Acoustic To Articulatory Speech Inversion Using Multi-Resolution Spectro-Temporal Representations Of Speech Signals
We used U. of Wisconsin X-ray Microbeam (XRMB) database of clean speech signals to train a feed-forward deep neural network (DNN) to estimate articulatory trajectories of six tract variables.
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing
Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform.
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins.
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Design of new drug compounds with target properties is a key area of research in generative modeling.
An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide
Understanding the structure and properties of refractory oxides are critical for high temperature applications.
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
We apply this scheme to a Hafnium dioxide (HfO2) dataset generated from a melt-quench ab initio molecular dynamics (AIMD) protocol.
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adversarial Auto-encoders for Speech Based Emotion Recognition
Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks such as digit classification and face recognition.
