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1
Inferring Pitch from Coarse Spectral Features ...
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2
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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4
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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5
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)
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14
Automated analysis of lexical features in Frontotemporal Degeneration
In: Cortex (2021)
Abstract: We implemented an automated analysis of lexical aspects of semi-structured speech produced by healthy elderly controls (n=37) and three patient groups with frontotemporal degeneration (FTD): behavioral variant FTD (n=74), semantic variant primary progressive aphasia (svPPA, n=42), and nonfluent/agrammatic PPA (naPPA, n=22). Based on previous findings, we hypothesized that the three patient groups and controls would differ in the counts of part-of-speech (POS) categories and several lexical measures. With a natural language processing program, we automatically tagged POS categories of all words produced during a picture description task. We further counted the number of wh-words, and we rated nouns for abstractness, ambiguity, frequency, familiarity, and age of acquisition. We also computed the cross-entropy estimation, where low cross-entropy indicates high predictability, and lexical diversity for each description. We validated a subset of the POS data that were automatically tagged with the Google Universal POS scheme using gold-standard POS data tagged by a linguist, and we found that the POS categories from our automated methods were more than 90% accurate. For svPPA patients, we found fewer unique nouns than in naPPA and more pronouns and wh-words than in the other groups. We also found high abstractness, ambiguity, frequency, and familiarity for nouns and the lowest cross-entropy estimation among all groups. These measures were associated with cortical thinning in the left temporal lobe. In naPPA patients, we found increased speech errors and partial words compared to controls, and these impairments were associated with cortical thinning in the left middle frontal gyrus. bvFTD patients’ adjective production was decreased compared to controls and was correlated with their apathy scores. Their adjective production was associated with cortical thinning in the dorsolateral frontal and orbitofrontal gyri. Our results demonstrate distinct language profiles in subgroups of FTD patients and validate our automated method of analyzing FTD patients’ speech.
Keyword: Article
URL: http://www.ncbi.nlm.nih.gov/pubmed/33640853
https://doi.org/10.1016/j.cortex.2021.01.012
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044033/
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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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