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1
Inferring Pitch from Coarse Spectral Features ...
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Global TIMIT Mandarin Chinese
Ding, Hongwei; Liao, Sishi; Zhan, Yuqing. - : Linguistic Data Consortium, 2021. : https://www.ldc.upenn.edu, 2021
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3
Second DIHARD Challenge Development - Eleven Sources
Ryant, Neville; Liberman, Mark; Fiumara, James. - : Linguistic Data Consortium, 2021. : https://www.ldc.upenn.edu, 2021
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Second DIHARD Challenge Development - SEEDLingS
Ryant, Neville; Liberman, Mark; Fiumara, James. - : Linguistic Data Consortium, 2021. : https://www.ldc.upenn.edu, 2021
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First DIHARD Challenge -- System Submissions and Scores ...
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First DIHARD Challenge -- System Submissions and Scores ...
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7
Second DIHARD Challenge Development - SEEDLingS ...
Liberman, Mark; Fiumara, James; Cieri, Christopher. - : Linguistic Data Consortium, 2021
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8
Second DIHARD Challenge Development - Eleven Sources ...
Liberman, Mark; Fiumara, James; Cieri, Christopher. - : Linguistic Data Consortium, 2021
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9
Automatic recognition of suprasegmentals in speech ...
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10
Global TIMIT Mandarin Chinese ...
Ding, Hongwei; Liao, Sishi; Zhan, Yuqing. - : Linguistic Data Consortium, 2021
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11
Digital Speech Analysis in Progressive Supranuclear Palsy and Corticobasal Syndromes
In: J Alzheimers Dis (2021)
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12
The Future of Computational Linguistics: On Beyond Alchemy
In: Front Artif Intell (2021)
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13
Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
In: NPJ Schizophr (2021)
Abstract: Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., “the,” “a,”). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.
Keyword: Article
URL: http://www.ncbi.nlm.nih.gov/pubmed/33990615
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121795/
https://doi.org/10.1038/s41537-021-00154-3
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14
Automated analysis of lexical features in Frontotemporal Degeneration
In: Cortex (2021)
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15
Automated Analysis of Digitized Letter Fluency Data
In: Front Psychol (2021)
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16
Tonal Complexes and Tonal Alignment
In: North East Linguistics Society (2021)
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17
Global TIMIT Learner Simple English
Ding, Hongwei; Liao, Sishi; Zhan, Yuqing. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020
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18
Global TIMIT Learner Treebank English
Luan, Huan; Wang, Yanhong; Feng, Hui. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020
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19
Global TIMIT Mandarin Chinese-Guanzhong Dialect
Jiang, Yue; Zhan, Juhong; Han, Hongjian. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020
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20
Probing Acoustic Representations for Phonetic Properties ...
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