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Found 9 papers, 6 papers with code
MTLens: Machine Translation Output Debugging
The performance of Machine Translation (MT) systems varies significantly with inputs of diverging features such as topics, genres, and surface properties.
An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation
Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies.
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free Metric
The findings suggest that NoRefER is not merely a tool for error detection but also a comprehensive framework for enhancing ASR systems' transparency, efficiency, and effectiveness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Generative Meta-Learning Robust Quality-Diversity Portfolio
The weights of the model are updated using a gradient-based optimizer.
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision
The common standard for quality evaluation of automatic speech recognition (ASR) systems is reference-based metrics such as the Word Error Rate (WER), computed using manual ground-truth transcriptions that are time-consuming and expensive to obtain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning
The self-supervised NoRefER exploits the known quality relationships between hypotheses from multiple compression levels of an ASR for learning to rank intra-sample hypotheses by quality, which is essential for model comparisons.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Efficient Machine Translation Corpus Generation
In this paper, the effect of prioritizing with the proposed method on the resulting MT corpus quality is presented versus scheduling hypotheses randomly.
EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only
The online learning capability of this system allows for dynamic adaptation to alterations in the domain or machine translation engines, thereby obviating the necessity for additional training.
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning
To solve the problem of limited availability of high quality training data, we propose a transfer learning technique which helps to improve model performance substantially.
