Name
Title | ||
|---|---|---|
Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS) |
Abstract | ||
|---|---|---|
•Quantifying coherence in speech identifies formal thought disorder automatically.•Manual transcription constrains research and practice applications.•Standard coherence estimates are vulnerable to automated transcription errors.•TARDIS - our novel method for estimating coherence - is robust to such errors.•TARDIS applies to both contextual and skip-gram semantic embeddings.•TARDIS better aligns with coherence estimates from professional transcripts.•This facilitates scalable, privacy-preserving automated coherence estimation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.jbi.2022.103998 | Journal of Biomedical Informatics |
Keywords | DocType | Volume |
Formal Thought Disorder,Coherence in Speech,Auditory Verbal Hallucination,Automatic Speech Recognition,Neural Word Embeddings,Natural Language Processing | Journal | 126 |
ISSN | Citations | PageRank |
1532-0464 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weizhe Xu | 1 | 0 | 1.01 |
| Weichen Wang | 2 | 0 | 0.34 |
| Jake Portanova | 3 | 0 | 0.34 |
| Ayesha Chander | 4 | 0 | 0.68 |
| Andrew T. Campbell | 5 | 8958 | 759.66 |
| Serguei Pakhomov | 6 | 0 | 0.34 |
| Dror Ben-Zeev | 7 | 0 | 0.34 |
| Trevor Cohen | 8 | 579 | 53.11 |