This paper addresses a classical but important problem: The coupling of lexical tones and sentence intonation in tonal languages, such as Chinese, focusing particularly on voice fundamental frequency (F1) contours of speech. It is important because it forms the basis of speech synthesis technology and prosody analysis. We provide a solution to the problem with a constrained tone transformation technique based on structural modeling of the F1 contours. This consists of transforming target values in pairs from norms to variants. These targets are intended to sparsely specify the prosodic contributions to the F1 contours, while the alignment of target pairs between norms and variants is based on underlying lexical tone structures. When the norms take the citation forms of lexical tones, the technique makes it possible to separate sentence intonation from observed F0 contours. When the norms take normative F0 contours, it is possible to measure intonation variations from the norms to the variants, both having identical lexical tone structures. This paper explains the underlying scientific and linguistic principles and presents an algorithm that was implemented on computers. The method's capability of separating and combining tone and intonation is evaluated through analysis and re-synthesis of several hundred observed F0 contours.
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http://dx.doi.org/10.1121/1.2165071 | DOI Listing |
Alzheimers Dement
December 2024
VUNO Inc., Seoul, Korea, Republic of (South)
Background: Clinical diagnosis of frontotemporal dementia (FTD) can be challenging, requiring an accurate tool dedicated to this diagnostic hurdle. Since FTD exhibits distinct regional atrophy patterns on magnetic resonance imaging (MRI), AI‐aided automated brain volume analysis could enhance the clinical diagnostic assessment of FTD, including the detection of the disease and the classification of subtypes, which encompass behavioral variant (BV), semantic variant (SV), and progressive non‐fluent aphasia (PNFA). In this study, we leverage automated brain volumetry software to approach both FTD detection and the differential diagnosis among its subtypes.
View Article and Find Full Text PDFBackground: The measurement of serum and cerebrospinal fluid (CSF) neurofilaments light chain (NfLs) has been proven promising in differentiating the behavioral variant frontotemporal dementia (bvFTD) from non‐neurodegenerative mimics, including primary psychiatric disorders and non‐progressive cognitive/behavioral changes. However, studies on this topic are based on clinical diagnosis, which remains challenging and potentially confounded by the overlapping clinical phenotypes. We investigated the role of NfLs in this field by classifying patients based on the presence/absence of pathological longitudinal brain volume changes.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Background: Minoritized groups are an understudied population in frontotemporal degeneration (FTD) research. Recently, we demonstrated distinct neuropsychiatric symptom profiles in Black relative to White individuals with FTD but cognition across these groups has not been reported. This knowledge gap has potential implications for the care of individuals with FTD from minoritized groups.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
VUNO Inc., Seoul, Korea, Republic of (South)
Background: Clinical diagnosis of frontotemporal dementia (FTD) can be challenging, requiring an accurate tool dedicated to this diagnostic hurdle. Since FTD exhibits distinct regional atrophy patterns on magnetic resonance imaging (MRI), AI‐aided automated brain volume analysis could enhance the clinical diagnostic assessment of FTD, including the detection of the disease and the classification of subtypes, which encompass behavioral variant (BV), semantic variant (SV), and progressive non‐fluent aphasia (PNFA). In this study, we leverage automated brain volumetry software to approach both FTD detection and the differential diagnosis among its subtypes.
View Article and Find Full Text PDFJ Affect Disord
December 2024
UCL Social Research Institute, University College London, London, UK. Electronic address:
Background: Research suggests that individuals' local social networks, norms of reciprocity and sense of belonging (their local social capital, henceforth LSC), can cushion the impact of adverse events on their mental health. However, to date, little research has explored the pathways through which LSC operates to buffer stressors, especially during major crises, e.g.
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