Outputs (5)

Acoustic Modelling From Raw Source and Filter Components for Dysarthric Speech Recognition (2022)
Journal Article
Yue, Z., Loweimi, E., Christensen, H., Barker, J., & Cvetkovic, Z. (2022). Acoustic Modelling From Raw Source and Filter Components for Dysarthric Speech Recognition. IEEE/ACM Transactions on Audio, Speech and Language Processing, 30, 2968-2980. https://d

Acoustic modelling for automatic dysarthric speech recognition (ADSR) is a challenging task. Data deficiency is a major problem and substantial differences between typical and dysarthric speech complicate the transfer learning. In this paper, we aim... Read More about Acoustic Modelling From Raw Source and Filter Components for Dysarthric Speech Recognition.

Dysarthric Speech Recognition From Raw Waveform with Parametric CNNs (2022)
Presentation / Conference Contribution
Yue, Z., Loweimi, E., Christensen, H., Barker, J., & Cvetkovic, Z. (2022, September). Dysarthric Speech Recognition From Raw Waveform with Parametric CNNs. Paper presented at Interspeech 2022, Incheon, Korea

Raw waveform acoustic modelling has recently received increasing attention. Compared with the task-blind hand-crafted features which may discard useful information, representations directly learned from the raw waveform are task-specific and potentia... Read More about Dysarthric Speech Recognition From Raw Waveform with Parametric CNNs.

RCT: Random consistency training for semi-supervised sound event detection (2022)
Presentation / Conference Contribution
Shao, N., Loweimi, E., & Li, X. (2022, September). RCT: Random consistency training for semi-supervised sound event detection. Paper presented at Interspeech 2022, Incheon, Korea

Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency. The integration of semi-supervised learning (SSL) largely mitigates such problem. This paper researches on several core mod... Read More about RCT: Random consistency training for semi-supervised sound event detection.

Raw Source and Filter Modelling for Dysarthric Speech Recognition (2022)
Presentation / Conference Contribution
Yue, Z., Loweimi, E., & Cvetkovic, Z. (2022). Raw Source and Filter Modelling for Dysarthric Speech Recognition. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp43922.

Acoustic modelling for automatic dysarthric speech recognition (ADSR) is a challenging task. Data deficiency is a major problem and substantial differences between the typical and dysarthric speech complicates transfer learning. In this paper, we bui... Read More about Raw Source and Filter Modelling for Dysarthric Speech Recognition.

Multi-Modal Acoustic-Articulatory Feature Fusion For Dysarthric Speech Recognition (2022)
Presentation / Conference Contribution
Yue, Z., Loweimi, E., Cvetkovic, Z., Christensen, H., & Barker, J. (2022). Multi-Modal Acoustic-Articulatory Feature Fusion For Dysarthric Speech Recognition. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing

Building automatic speech recognition (ASR) systems for speakers with dysarthria is a very challenging task. Although multi-modal ASR has received increasing attention recently, incorporating real articulatory data with acoustic features has not been... Read More about Multi-Modal Acoustic-Articulatory Feature Fusion For Dysarthric Speech Recognition.