The effect of linguistic experience on perceived vowel duration: Evidence from Taiwan Mandarin speakers

Lu, Yuan; Lee-Kim, Sang-Im

doi:10.1016/j.wocn.2021.101049

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…All tokens were scaled to 75 dB using Praat (Boersma and Weenink, 2017). These syllables were then resynthesized using Tandem-Straight, a speech analysis, modification, and resynthesis framework that allows the pitch, duration and voice quality of the entire syllable to be manipulated proportionally (Kawahara et al, 2008), as secondary cues such as duration and creakiness have also been shown to affect listeners' perception and categorization of tones (Yu, 2010;Wu and Kenstowicz, 2015;Lu and Lee-Kim, 2021). In their work on tonal categorization, Yang et al (2019) describe the advantages of resynthesizing pitch along with other secondary cues over only resynthesizing pitch.…”

Section: Methodsmentioning

confidence: 99%

“…Note that there are two T3 variants, full T3 (X 214 ) and half T3 (X 21 ), with the latter variant observed more frequently in Taiwan Mandarin (e.g., Kubler, 1985;Fon and Chiang, 1999). However, the variant with the dipping contour has nevertheless been shown to be the canonical representation of T3 for Taiwan Mandarin speakers (Lu and Lee-Kim, 2021). We thus used the full T3 variant as the stimuli for continuum resynthesis involving T3 (Figure 1, lower panel, leftmost, and rightmost plots) except for those in the T1-T3 continua.…”

Section: Methodsmentioning

confidence: 99%

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The Effect of Lexicality, Frequency, and Markedness on Mandarin Tonal Categorization

Yang

Jin

2022

Front. Psychol.

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While the Ganong lexicality effect has been observed for phonemic and tonal categorization, the effects of frequency and markedness are less clear, especially in terms of tonal categorization. In this study, we use Mandarin Chinese to investigate the effects of lexicality, tone frequency and markedness. We examined Mandarin speakers’ tonal categorization of tokens on all possible tonal continua with one end being a word and the other being a tonotactic gap (i.e., an unattested syllable-tone combination). The results of a forced-choice identification experiment showed a general bias against the gap endpoints, with the noted exception of continua involving T4 (X51), the most frequent lexical tone. Specifically, when T4 served as the gap endpoint, no obvious bias against it was observed regardless of its lexical status. Moreover, on the T3–T4 continua, there was an apparent bias against T3 (X214), the tone with the most complex contour, again, regardless of lexicality, suggesting a strong markedness effect. Taken together, the results of this study show the individual effects of lexicality, tone frequency and markedness, as well as their interactions, which contribute to our understanding of tonal categorization in relation to lexical statistics (tone frequency) and phonology (markedness).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Effect of Lexicality, Frequency, and Markedness on Mandarin Tonal Categorization

Yang

Jin

2022

Front. Psychol.

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“…Mitterer and Ernestus, 2008; Nielsen, 2011). Phonetic cues are imitated differently according to their relevance in one’s native phonological system (Kim and Clayards, 2019; Kwon, 2019; Lu and Lee-Kim, 2021). Since Mandarin Chinese contrasts aspiration, and the average VOTs of aspirated and unaspirated stops fall well within the range of those in English (Table 4), we expect that VOT cues related to unaspirated and aspirated stops will be successfully imitated by Mandarin speakers.…”

Section: Imitation Experimentsmentioning

confidence: 99%

The effect of orthography in Mandarin speakers’ production of English voiceless stops

Lee

2023

Second Language Research

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This article provides a review of previous studies that have examined the effects of orthography on establishing contrastive phonological representations in second language acquisition and presents results from an original study on Mandarin speakers’ production of English stops investigating how the presence of orthography affects the production of phonological categories that involve allophony. English voiceless stops are canonically represented as aspirated [ph, th, kh] in word-initial/stressed onset positions but are realized as unaspirated [p, t, k] following /s/ and in unstressed, non-initial onset positions. The results of our imitation experiment showed that Mandarin speakers failed to correctly imitate the unaspirated allophones when presented with written input, and this orthographic effect was stronger with nonwords than with real words. These results are best explained by an orthography effect mediated by phonological awareness and prior linguistic experience.

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“…The differences in phonemes between one's mother tongue and the new language are essential factors in pronunciation practice. Previous studies [21][22][23][24][25] discovered that learners have difficulty with the pronunciation of vowels when learning second and third languages.…”

Section: Vowel's Pronunciation Problemsmentioning

confidence: 99%

An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition

Rukwong

Pongpinigpinyo

2022

Applied Sciences

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For Thai vowel pronunciation, it is very important to know that when mispronunciation occurs, the meanings of words change completely. Thus, effective and standardized practice is essential to pronouncing words correctly as a native speaker. Since the COVID-19 pandemic, online learning has become increasingly popular. For example, an online pronunciation application system was introduced that has virtual teachers and an intelligent process of evaluating students that is similar to standardized training by a teacher in a real classroom. This research presents an online automatic computer-assisted pronunciation training (CAPT) using deep learning to recognize Thai vowels in speech. The automatic CAPT is developed to solve the inadequacy of instruction specialists and the complex vowel teaching process. It is a unique system that develops computer techniques integrated with linguistic theory. The deep learning model is the most significant part of recognizing vowels pronounced for the automatic CAPT. The major challenge in Thai vowel recognition is the correct identification of Thai vowels when spoken in real-world situations. A convolutional neural network (CNN), a deep learning model, is applied and developed in the classification of pronounced Thai vowels. A new dataset for Thai vowels was designed, collected, and examined by linguists. The result of an optimal CNN model with Mel spectrogram (MS) achieves the highest accuracy of 98.61%, compared with Mel frequency cepstral coefficients (MFCC) with the baseline long short-term memory (LSTM) model and MS with the baseline LSTM model have an accuracy of 94.44% and 90.00% respectively.

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The effect of linguistic experience on perceived vowel duration: Evidence from Taiwan Mandarin speakers

Cited by 6 publications

References 29 publications

The Effect of Lexicality, Frequency, and Markedness on Mandarin Tonal Categorization

The Effect of Lexicality, Frequency, and Markedness on Mandarin Tonal Categorization

The effect of orthography in Mandarin speakers’ production of English voiceless stops

An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition

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