A multi-level analysis of spoken discourse production in healthy Cantonese speaking adults
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1
University of Central Florida, Department of Communication Sciences and Disorders, United States
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2
University of Hong Kong, Division of Speech and Hearing Sciences, Hong Kong, SAR China
Introduction:
Assessing oral discourse is clinically informative for management of aphasia. Most previous studies have focused on discrete levels of linguistic analysis of aphasic output. The multi-level analytic approach of spoken discourse (Marini, Andreetta, del Tin, & Carlomagno, 2011; Sherratt, 2007) has recently gained increasing interest and attention. This framework is characterized by the combined structural and functional analyses of connected speech that simultaneously target one’s lexical-, syntactic-, and discourse-level performance.
Aims:
This study aimed to adopt a multi-level analytic approach to quantify discourse samples of healthy speakers taken from Cantonese AphasiaBank (Kong & Law, 2018). Specifically, we addressed (RQ1) how different linguistic levels (i.e., lexical, syntactic, and discourse levels) contributed to discourse informativeness, and (RQ2) whether age and education affected discourse performance.
Methods:
Transcripts of the ‘Cat Rescue’ single picture and ‘Broken Window’ picture sequence of 150 unimpaired speakers were extracted. The participants were stratified into three age subgroups and two education subgroups. Each sample was analyzed with 16 indices:
• Lexical level: (L1) Type-token ratio (TTR) of content word, (L2) Proportion of closed-class words, (L3) Content word errors, and (L4) Function word errors
• Syntactic level: (S1) Noun phrase expansion, (S2) Verb phrase (VP) expansion, (S3) Complex sentence, (S4) Syntactic error
• Discourse level: (D1) Local coherence errors, (D2) Global coherence errors, (D3) Story grammar total score, (D4) Story grammar completeness
• Informativeness: (I1) Main Concept score, (I2) Accurate and Complete concepts per minute (AC/min), (I3) Correct Information Units (CIU) per minute, (I4) CIU per word
For RQ1, Pearson correlation coefficients were computed for the standardized scores of the 16 indices to explore how informativeness was related to various linguistic measures. Significantly correlated measures were then entered in multiple regression analyses to examine how different linguistic measures would contribute to discourse informativeness in content (I1) and efficiency (I2).
For RQ2, two-way MANOVAs were conducted to evaluate effects of age, education, and their interaction on performance of different linguistic measures.
Results:
Results of the multiple regression models showed that indices of different linguistic levels predicted (I1) and (I2). Concerning the single picture description, story grammar total score, VP expansion, and TTR significantly predicted (I1), explaining 48.8% of variance. In contrast, predictors including TTR, proportion of closed-class words, and global coherence errors accounted for only 15.5% of variance of (I2). For the sequential picture description, story grammar total score, VP expansion, TTR, complex sentences, and local coherence errors were significant predictors for I1, explaining 36.1% of variance. Finally, global coherence errors, proportion of closed-class words, VP expansion, and local coherence errors significantly predicted I2, accounting for 25.9% of variance (Table 1).
MANOVAs revealed a significant main effect of education for both tasks: Pillai’s Trace for single picture description = .234 [F(16,129)=2.459, p<.05, η2=.234], and for sequential picture description = .226 [F(16,129)=2.360, p<.01, η2=.226]. Follow-up univariate analyses found better performance by more highly educated participants on global coherence errors, story grammar total score and completeness in single picture description, and Main Concept score and CIU/minute in picture sequence description (ps<.003).
Acknowledgements
This study is supported by a grant funded by the National Institutes of Health to Anthony Pak-Hin Kong (PI) and Sam-Po Law (Co-I) [project number: NIH-R01-DC010398].
References
Kong, A.P.H. & Law, S.P. (2018). Cantonese AphasiaBank: An annotated database of spoken discourse and co-verbal gestures by healthy and language-impaired native Cantonese speakers. Behavior Research Methods. doi: 10.3758/s13428-018-1043-6
Marini, A., Andreetta, S., Del Tin, S., & Carlomagno, S. (2011). A multi-level approach to the analysis of narrative language in aphasia. Aphasiology, 25, 1372–1392.
Sherratt, S. (2007). Multi-level discourse analysis: A feasible approach. Aphasiology, 21, 375–393.
Keywords:
discourse,
multi-level analysis,
Cantonese,
Healthy adults,
Cantonese AphasiaBank
Conference:
Academy of Aphasia 56th Annual Meeting, Montreal, Canada, 21 Oct - 23 Oct, 2018.
Presentation Type:
poster presentation
Topic:
not eligible for a student prize
Citation:
Kong
A,
Law
S,
Lo
L and
Li
W
(2019). A multi-level analysis of spoken discourse production in healthy Cantonese speaking adults.
Conference Abstract:
Academy of Aphasia 56th Annual Meeting.
doi: 10.3389/conf.fnhum.2018.228.00048
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Received:
16 Apr 2018;
Published Online:
22 Jan 2019.
*
Correspondence:
Prof. Anthony Pak Hin Kong, University of Central Florida, Department of Communication Sciences and Disorders, Orlando, United States, akong@hku.hk