The role of talker variability in the perceptual learning of Mandarin tones by American English listeners

Abstract

Research on segmentals has suggested that a key component of High Variability Phonetic Training (HVPT) is high talker variability. However, the extent to which high talker variability improves perception of tones is unclear. This study examined the effects of high talker variability on the perception of Mandarin tones (Tones 1–4) by English-speaking listeners. A training paradigm that used multiple talkers (multitalker group) was compared with a paradigm that used one talker (single-talker group). The results showed that the multitalker group outperformed the single-talker group, and they retained their learning better than the single-talker group did for 6 months. Neither group, however, improved their perception of Tone 1 or generalized their learning of monosyllables to disyllables. The results suggest that although high talker variability can effectively improve tone perception, it does not improve the perception of more tone categories or yield generalization of learning to more contexts compared to low talker variability.

Cognitive Factors in Nonnative Phonetic Learning: Impacts of Working Memory and Selective Attention on the Benefits and Costs of Talker Variability

“…One study was conducted by Macdonald (2012) who found that only multiple-talker training effects were retained one month later when training native English-speaking learners on French vowel contrasts (/u/ vs. /y/ and /ɑ/ vs. /ɔ/). Another study is Silpachai (2020) who trained English speakers who have limited tonal language experience to perceive Mandarin tones (Tones 1-4). The results showed that the multiple-talker group retained their learning of Tone 2, 3, and 4 six months after training, while the single-talker group also retained the learning of Tone 3 and 4.…”

“…One complication here is that increased variability may come at a processing cost Fuhrmeister & Myers, 2017, 2020Luthra et al, 2021;Sadakata & Mcqueen, 2014;Saltzman et al, 2021). Across domains of visual perception, auditory perception, motor learning, language, inductive reasoning, problem solving, and computational modeling, a general observation is that increased input variability may come at a cost of initially hindering learning but often show subsequent benefits in generalization (Raviv et al, 2022).…”