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Found 11 papers, 6 papers with code
A single speaker is almost all you need for automatic speech recognition
We explore the use of speech synthesis and voice conversion applied to augment datasets for automatic speech recognition (ASR) systems, in scenarios with only one speaker available for the target language.
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS.
Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyond
Training deep neural networks may be challenging in real world data.
SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model
In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model that improves similarity for speakers unseen during training.
TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese
Speech provides a natural way for human-computer interaction.
Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models
We compare the three best architectures trained using our method to select the best one, which is the one with a shallow architecture.
Generalization of feature embeddings transferred from different video anomaly detection domains
By proposing novel cross-domain generalization measures, we study how source features can generalize for different target video domains, as well as analyze unsupervised transfer learning.
Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis
The models are evaluated considering both the reconstruction error of the images and the feature spaces in terms of their discriminative power.
Como funciona o Deep Learning
Deep Learning methods are currently the state-of-the-art in many problems which can be tackled via machine learning, in particular classification problems.
Computing the Shattering Coefficient of Supervised Learning Algorithms
The Statistical Learning Theory (SLT) provides the theoretical guarantees for supervised machine learning based on the Empirical Risk Minimization Principle (ERMP).
Providing theoretical learning guarantees to Deep Learning Networks
Deep Learning (DL) is one of the most common subjects when Machine Learning and Data Science approaches are considered.
