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Found 23 papers, 13 papers with code
How Temporal Unrolling Supports Neural Physics Simulators
We also quantify a difference in the accuracy of models trained in a fully differentiable setup compared to their non-differentiable counterparts.
The North System for Formosa Speech Recognition Challenge 2023
This report provides a concise overview of the proposed North system, which aims to achieve automatic word/syllable recognition for Taiwanese Hakka (Sixian).
Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
We find that even simple diffusion-based approaches can outperform multiple established flow prediction methods in terms of accuracy and temporal stability, while being on par with state-of-the-art stabilization techniques like unrolling at training time.
Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings
The use of positional embeddings in transformer language models is widely accepted.
Speaker-Independent Acoustic-to-Articulatory Speech Inversion
To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interpretable space.
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech
Recent Text-to-Speech (TTS) systems trained on reading or acted corpora have achieved near human-level naturalness.
A unified one-shot prosody and speaker conversion system with self-supervised discrete speech units
To address these issues, we devise a cascaded modular system leveraging self-supervised discrete speech units as language representation.
A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech
The lack of clean speech is a practical challenge to the development of speech enhancement systems, which means that there is an inevitable mismatch between their training criterion and evaluation metric.
Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss-functions and Optimisation Horizons
In this paper, we train turbulence models based on convolutional neural networks.
Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs
Simulations that produce three-dimensional data are ubiquitous in science, ranging from fluid flows to plasma physics.
Fine-grained style control in Transformer-based Text-to-speech Synthesis
In this paper, we present a novel architecture to realize fine-grained style control on the transformer-based text-to-speech synthesis (TransformerTTS).
Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition
While Wav2Vec 2. 0 has been proposed for speech recognition (ASR), it can also be used for speech emotion recognition (SER); its performance can be significantly improved using different fine-tuning strategies.
Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils
The present study investigates the accurate inference of Reynolds-averaged Navier-Stokes solutions for the compressible flow over aerofoils in two dimensions with a deep neural network.
Spectral Analysis for Semantic Segmentation with Applications on Feature Truncation and Weak Annotation
We propose a spectral analysis to investigate the correlations among the resolution of the down sampled grid, the loss function and the accuracy of the SSNNs.
Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates
In the present study, U-net based deep neural network (DNN) models are trained with high-fidelity datasets to infer flow fields, and then employed as surrogate models to carry out the shape optimisation problem, i. e. to find a drag minimal profile with a fixed cross-section area subjected to a two-dimensional steady laminar flow.
Fluid Dynamics
Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach
Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, question answering and so on.
Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech
This paper focuses on using voice conversion (VC) to improve the speech intelligibility of surgical patients who have had parts of their articulators removed.
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Existing neural semantic parsers mainly utilize a sequence encoder, i. e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees.
