Abstract
In this paper, we explore the effectiveness of deploying the raw phase and magnitude spectra for dysarthric speech recognition, detection and classification. In particular, we scrutinise the usefulness of various raw phase-based representations along with their combinations with the raw magnitude spectrum and filterbank features. We employed single and multi-stream architectures consisting of a cascade of convolutional, recurrent and fully-connected layers for acoustic modelling. Furthermore, we investigate various configurations and fusion schemes as well as their training dynamics. In addition, the accuracies of the raw phase and magnitude based systems in the detection and classification tasks are studied and discussed. We report the performance on the UASpeech and TORGO dysarthric speech databases and for different severity levels. Our best system achieved WERs of 31.2% and 9.1% for dysarthric and typical speech on TORGO and 30.2% on UASpeech, respectively.
| Original language | English |
|---|---|
| Pages (from-to) | 1533-1537 |
| Number of pages | 5 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Volume | 2023-August |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 24th International Speech Communication Association, Interspeech 2023 - Dublin, Ireland Duration: 20 Aug 2023 → 24 Aug 2023 |
Keywords
- Dysarthric speech processing
- raw phase and magnitude spectra
- single- and multi-stream acoustic modelling