A5039365041- Speech Recognition and Synthesis
- Speech and dialogue systems
- Natural Language Processing Techniques
- Topic Modeling
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Music and Audio Processing
- Diverse Approaches in Healthcare and Education Studies
- Interpreting and Communication in Healthcare
- Technology and Data Analysis
- Robotics and Automated Systems
- Educational Technology and Assessment
- Neural Networks and Applications
- Innovation in Digital Healthcare Systems
- Explainable Artificial Intelligence (XAI)
University of Edinburgh
2023-2024
Aalto University
2022-2024
SpeechBrain is an open-source Conversational AI toolkit based on PyTorch, focused particularly speech processing tasks such as recognition, enhancement, speaker text-to-speech, and much more. It promotes transparency replicability by releasing both the pre-trained models complete "recipes" of code algorithms required for training them. This paper presents 1.0, a significant milestone in evolution toolkit, which now has over 200 recipes speech, audio, language tasks, more than 100 available...
Traditionally, teaching a human and Machine Learning (ML) model is quite different, but organized structured learning has the ability to enable faster better understanding of underlying concepts. For example, when humans learn speak, they first how utter basic phones then slowly move towards more complex structures such as words sentences. Motivated by this observation, researchers have started adapt approach for training ML models. Since main concept, gradual increase in difficulty,...
Abstract The successful application of machine learning (ML) methods increasingly depends on their interpretability or explainability. Designing explainable ML (XML) systems is instrumental for ensuring transparency automated decision-making that targets humans. explainability also an essential ingredient trustworthy artificial intelligence. A key challenge in its dependence the specific human end user system. users might have vastly different background knowledge about principles, with some...
It is common knowledge that the quantity and quality of training data play a significant role in creation good machine learning model.In this paper, we take it one step further demonstrate way examples are arranged also crucial importance.Curriculum Learning built on observation organized structured assimilation has ability to enable faster better comprehension.When humans learn speak, they first try utter basic phones then gradually move towards more complex structures such as words...
Traditionally, teaching a human and Machine Learning (ML) model is quite different, but organized structured learning has the ability to enable faster better understanding of underlying concepts. For example, when humans learn speak, they first how utter basic phones then slowly move towards more complex structures such as words sentences. Motivated by this observation, researchers have started adapt approach for training ML models. Since main concept, gradual increase in difficulty,...
It is common knowledge that the quantity and quality of training data play a significant role in creation good machine learning model. In this paper, we take it one step further demonstrate way examples are arranged also crucial importance. Curriculum Learning built on observation organized structured assimilation has ability to enable faster better comprehension. When humans learn speak, they first try utter basic phones then gradually move towards more complex structures such as words...