@inproceedings{gale-EtAl:2022:RaPID4,
 abstract = {We present the outcome of the Post-Stroke Speech Transcription (PSST) challenge. For the challenge, we prepared a new data resource of responses to two confrontation naming tests found in AphasiaBank, extracting audio and adding new phonemic transcripts for each response. The challenge consisted of two tasks. Task A asked challengers to build an automatic speech recognizer (ASR) for phonemic transcription of the PSST samples, evaluated in terms of phoneme error rate (PER) as well as a finer-grained metric derived from phonological feature theory, feature error rate (FER). The best model had a 9.9% FER / 20.0% PER, improving on our baseline by a relative 18% and 24%, respectively. Task B approximated a downstream assessment task, asking challengers to identify whether each recording contained a correctly pronounced target word. Challengers were unable to improve on the baseline algorithm; however, using this algorithm with the improved transcripts from Task A resulted in 92.8% accuracy / 0.921 F1, a relative improvement of 2.8% and 3.3%, respectively.},
 address = {Marseille, France},
 author = {Gale, Robert C. and Fleegle, Mikala and Fergadiotis, Gerasimos and Bedrick, Steven},
 bdsk-url-1 = {https://aclanthology.org/2022.rapid4-1.6},
 booktitle = {Proceedings of The RaPID4 Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference},
 date-added = {2022-07-07 17:40:25 -0700},
 date-modified = {2025-01-10 16:56:20 -0800},
 month = {June},
 pages = {41--55},
 publisher = {European Language Resources Association},
 title = {The Post-Stroke Speech Transcription (PSST) Challenge},
 url = {https://aclanthology.org/2022.rapid-1.6/},
 year = {2022}
}