Deep Learning Based Speech Quality Prediction
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Gabriel Mittag received his B.Sc. and M.Sc. degree in electrical and electronic engineering at the Technische Universität Berlin. During his master's degree he spent two semesters at the RMIT University in Melbourne, Australia and focused primarily on biomedical and speech signal processing. From 2016 he was employed as research assistant at the Quality and Usability Lab at the TU Berlin, where he finished his PhD on the machine learning based prediction of speech quality. In May 2021, Gabriel Mittag started as Machine Learning Scientist at Microsoft in Redmond, WA, USA.
Content
1. Introduction.- 2. Quality Assessment of Transmitted Speech.- 3. Neural Network Architectures for Speech Quality Prediction.- 4. Double-Ended Speech Quality Prediction Using Siamese Networks.- 5. Prediction of Speech Quality Dimensions With Multi-Task Learning.- 6. Bias-Aware Loss for Training From Multiple Datasets.- 7. NISQA - A Single-Ended Speech Quality Model.- 8. Conclusions.- A. Dataset Condition Tables.- B. Train and Validation Dataset Dimension Histograms.- References.
