Improving whispered speech recognition using pseudo-whispered based data augmentation

Whispering, characterized by its soft, breathy, and hushed qualities, serves as a distinct form of speech commonly employed for private communication and can also occur in cases of pathological speech. The acoustic characteristics of whispered speech differ substantially from normally phonated speech and the scarcity of adequate training data...

Mitigating Regional Accent Bias in ASR Systems

End-to-end Automatic Speech Recognition (ASR) systems improved drastically in recent years and they work extremely well on many large datasets. However, research shows that these models failed to capture the variability in speech production and have biases against the variant caused by the regional accented speech. Moreover, ASR research on...

Bias Mitigation Against Non-native Speakers in Dutch ASR

One of the most important problems that needs tackling for wide deployment of Automatic Speech Recognition (ASR) is the bias in ASR, i.e., ASRs tend to generate more accurate predictions for certain speaker groups while making more errors on speech from others. In this thesis, we aim to reduce bias against non-native speakers of Dutch compared...

Automatic speech recognition using dynamic Bayesian networks

New ideas to improve automatic speech recognition have been proposed that make use of context user information such as gender, age and dialect. To incorporate this information into a speech recognition system a new framework is being developed at the MMI department of the EWI faculty at the Delft University of Technology. This toolkit is called...