Name
Title | ||
|---|---|---|
Combining Phone Posteriorgrams From Strong And Weak Recognizers For Automatic Speech Assessment Of People With Aphasia |
Abstract | ||
|---|---|---|
This paper presents an investigation on applying automatic speech recognition (ASR) to speech assessment of people with aphasia (PWA). A distinctive characteristic of PWA speech is paraphasia, which refers to frequent occurrence of phonemic errors, unintended words and non-verbal sounds. In view of the wide variety of paraphasias, we propose to view the ASR errors so caused as out-of-vocabulary (OOV) words. Inspired by previous research on OOV detection, paraphasias in PWA speech are captured by comparing the phone posteriorgrams of a strongly constrained speech recognizer and a weakly constrained one. The posteriorgrams also reveal other characteristics of impaired speech, e.g., change of speaking rate, voice abnormality. Siamese and 2-channel convolutional neural network (CNN) models are used for classifying the posteriorgram pairs and predicting the severity of aphasia. Experimental results on a Cantonese database of PWA speech confirm the effectiveness of the proposed methods. The best F1 score attained on binary classification (severe versus mild aphasia) is 0:891. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683835 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Aphasia, speech assessment, ASR, phone posteriorgrams, CNN | F1 score,Automatic speech,Pattern recognition,Binary classification,Convolutional neural network,Computer science,Impaired speech,Aphasia,Speech recognition,Phone,Artificial intelligence,Paraphasia | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Qin | 1 | 1 | 5.43 |
| Tan Lee | 2 | 476 | 74.69 |
| anthony pak hin kong | 3 | 0 | 2.03 |