Search Results for author:
Found 7 papers, 4 papers with code
Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks
Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of medical issues that may need prompt interventions.
Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors
The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings.
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition
In this paper, we combine CPC and multiple dimensionality reduction methods in search of functioning practices for clustering-based AL. Our experiments for simulating speech emotion recognition system deployment show that both the local and global topology of the feature space can be successfully used for AL, and that CPC can be used to improve clustering-based AL performance over traditional signal features.
Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors
In addition, we explore the benefits of data augmentation methods in ideal and non-ideal recording conditions.
Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors
These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group.
Speaker-independent raw waveform model for glottal excitation
Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i. e., generating speech waveforms from acoustic features.
Speech waveform synthesis from MFCC sequences with generative adversarial networks
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis.
