Do speakers of all languages use segmental speech sounds when they produce words? Existing models of language production generally assume a mental representation of individual segmental units, or phonemes, but the bulk of evidence comes from speakers of European languages in which the orthographic system codes explicitly for speech sounds. By contrast, in languages with nonalphabetical scripts, such as Mandarin Chinese, individual speech sounds are not orthographically represented, raising the possibility that speakers of these languages do not use phonemes as fundamental processing units. We used event-related potentials (ERPs) combined with behavioral measurement to investigate the role of phonemes in Mandarin production. Mandarin native speakers named colored line drawings of objects using color adjective-noun phrases; color and object name either shared the initial phoneme or were phonologically unrelated. Whereas naming latencies were unaffected by phoneme repetition, ERP responses were modulated from 200 ms after picture onset. Our ERP findings thus provide strong support for the claim that phonemic segments constitute fundamental units of phonological encoding even for speakers of languages that do not encode such units orthographically.
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http://dx.doi.org/10.1073/pnas.1200632109 | DOI Listing |
Behav Res Methods
January 2025
Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, No.19 Xinjiekouwai Street, Haidian District, Beijing, 100875, China.
Over the past few decades, Swahili-English and Lithuanian-English word pair databases have been extensively utilized in research on learning and memory. However, these normative databases are specifically designed for generating study stimuli in learning and memory research involving native (or fluent) English speakers. Consequently, they are not suitable for investigations that encompass populations whose first language is not English, such as Chinese individuals.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Miin Wu School of Computing, National Cheng Kung University, Tainan, Taiwan.
Background: Alzheimer's disease (AD) has been associated with speech and language impairment. Recent progress in the field has led to the development of automated AD detection using audio-based methods, because it has a great potential for cross-linguistic detection. In this investigation, we utilised a pretrained deep learning model to automatically detect AD, leveraging acoustic data derived from Chinese speech.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Miin Wu School of Computing, National Cheng Kung University, Tainan, Taiwan.
Background: Continuous speech analysis is considered as an efficient and convenient approach for early detection of Alzheimer's Disease (AD). However, the traditional approach generally requires human transcribers to transcribe audio data accurately. This study applied automatic speech recognition (ASR) in conjunction with natural language processing (NLP) techniques to automatically extract linguistic features from Chinese speech data.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Cognitive Neuroscience Center, University of San Andrés, Victoria, Buenos Aires, Argentina.
Background: Automated speech and language analysis (ASLA) represents a powerful innovation for detecting and monitoring persons with or at risk for dementia. Given its cost-efficiency and automaticity, its impact can be vital for under-resourced communities, such Spanish-speaking Latinos. However, ASLA markers are understudied in this group and may differ from those established in widely studied populations (e.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Background: Primary Progressive Aphasia (PPA) is a neurodegenerative disorder primarily affecting language abilities, with clinical variants (nonfluent/agrammatic variant [nfvPPA], semantic variant [svPPA], logopenic variant [lvPPA], and mixed-PPA [mPPA]) categorized based on linguistic features. This study aims to compare PPA cohorts of native speakers of two different languages: English (an analytic language with deep orthography) and Italian (a synthetic language with shallow orthography).
Methods: We considered 166 English participants (70 nfvPPA, 45 svPPA, 42 lvPPA, 9 mPPA) and 106 Italian participants (14 nfvPPA, 20 svPPA, 42 lvPPA, 31 mPPA).
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