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Found 16 papers, 2 papers with code
Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation
In this paper, we investigate whether we can control prosody directly from the input text, in order to code information related to contrastive focus which emphasizes a specific word that is contrary to the presuppositions of the interlocutor.
Transformers in Speech Processing: A Survey
The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences.
MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection
Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i. e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net).
A Survey on Deep Reinforcement Learning for Audio-Based Applications
We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain.
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition
Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Disentangled Representation Learning with Information Maximizing Autoencoder
Learning disentangled representation from any unlabelled data is a non-trivial problem.
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding
The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis.
Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
Cross-lingual speech emotion recognition is an important task for practical applications.
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness
Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems.
Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks
Multishot Magnetic Resonance Imaging (MRI) has recently gained popularity as it accelerates the MRI data acquisition process without compromising the quality of final MR image.
Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection
Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise.
Using Deep Autoencoders for Facial Expression Recognition
Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature.
Dimensionality Reduction
Facial Expression Recognition (FER)
Soft Computing Techniques for Dependable Cyber-Physical Systems
Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements.
Transfer Learning for Improving Speech Emotion Classification Accuracy
The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions.
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study
Inspired by this, we propose VAEs for deriving the latent representation of speech signals and use this representation to classify emotions.
