JOURNAL ARTICLE

Categorical perception of Mandarin tones by Chinese-native, English-native and Chinese-as-a-second-language English listeners

Wenyi LingTheres GrüterAmy J. Schafer

Year: 2016 Journal:   The Journal of the Acoustical Society of America Vol: 140 (4_Supplement)Pages: 3334-3334   Publisher: Acoustical Society of America

Abstract

Previous research found that native-Chinese listeners perceive tones more categorically than listeners with no knowledge of a tonal language (naïve listeners; Hallé et al., 2004). This study examined tone perception by 26 native-English adult learners of Mandarin (L2ers) in comparison to 30 naïve and 30 native-Chinese listeners. Identification and AXB discrimination tasks tested monosyllables (/pi/, /pa/) with 9-step F0 continua between all possible Mandarin tone pairs. Native listeners showed more categorical identification (steeper slopes) than naïve listeners. The L2 group showed significantly shallower identification slopes than native listeners (p<0.01) and steeper slopes than naïve listeners (p<0.01). L2 proficiency (listening test plus self-report) positively correlated with identification performance (r = 0.36, z=-2.56, p<0.01), suggesting higher proficiency may lead to more native-like tone identification. However, although L2ers’ discrimination accuracy (0.90) was significantly higher than native listeners’ (0.82; z = -4.76, p<. 001), it did not differ significantly from naïve listeners’ (0.90; z = -0.61, p = 0.54), suggesting that the non-native groups discriminated tone pair continua in a similar way. Tone pairs involving level tones (e.g., T1-T3) showed more categorical patterns than those involving two contour tones (e.g., T2-T3) in each group, consistent with the Anchor Hypothesis (Xu et al., 2006).

Keywords:
Mandarin Chinese Tone (literature) Psychology Active listening Linguistics First language Perception Second language Audiology Acoustics Mathematics Speech recognition Communication Physics Computer science Medicine Philosophy

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Topics

Hearing Loss and Rehabilitation
Life Sciences →  Neuroscience →  Cognitive Neuroscience
Speech and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
Blind Source Separation Techniques
Physical Sciences →  Computer Science →  Signal Processing

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