Language attitudes modulate phonetic interactions between languages in bilingual speakers in diglossic
                    settings

Law, Wai Ling; Dmitrieva, Olga; Francis, Alexander L.

doi:10.1075/lab.18085.law

Cited by 5 publications

(3 citation statements)

References 67 publications

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“…Additionally, learners' motivations for studying the chosen language and their global attitudes towards the associated culture and native speakers of their L2 could further mediate the propensity to drift. Previous research has shown that language attitudes and considerations of prestige can influence cross-language interaction in bilinguals (Gatbonton et al 2005;Gatbonton et al 2011;Giles et al 1977;Law et al 2019), but considerably more work is needed to determine the role of such factors in L2-to-L1 phonetic effects.…”

Section: Discussionmentioning

confidence: 99%

The Effect of Instructed Second Language Learning on the Acoustic Properties of First Language Speech

2020

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This paper reports on a comprehensive phonetic study of American classroom learners of Russian, investigating the influence of the second language (L2) on the first language (L1). Russian and English productions of 20 learners were compared to 18 English monolingual controls focusing on the acoustics of word-initial and word-final voicing. The results demonstrate that learners’ Russian was acoustically different from their English, with shorter voice onset times (VOTs) in [−voice] stops, longer prevoicing in [+voice] stops, more [−voice] stops with short lag VOTs and more [+voice] stops with prevoicing, indicating a degree of successful L2 pronunciation learning. Crucially, learners also demonstrated an L1 phonetic change compared to monolingual English speakers. Specifically, the VOT of learners’ initial English voiceless stops was shortened, indicating assimilation with Russian, while the frequency of prevoicing in learners’ English was decreased, indicating dissimilation with Russian. Word-final, the duration of preceding vowels, stop closures, frication, and voicing during consonantal constriction all demonstrated drift towards Russian norms of word-final voicing neutralization. The study confirms that L2-driven phonetic changes in L1 are possible even in L1-immersed classroom language learners, challenging the role of reduced L1 use and highlighting the plasticity of the L1 phonetic system.

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Section: Discussionmentioning

confidence: 99%

The Effect of Instructed Second Language Learning on the Acoustic Properties of First Language Speech

2020

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“…Additionally, socio-phonetic factors may have contributed to the results of the present study. Although we tried to control for a number of factors known to affect speech production such as gender and dialect, other factors were not controlled (e.g., attitude towards the bilinguals' languages; Law et al, 2021). Basque is a minority language, and a good command of Basque is important for Basque identity (Azurmendi et al, 2008).…”

Section: Vot Production In Basque-spanish Bilingualsmentioning

confidence: 99%

Bilinguals produce language-specific voice onset time in two true-voicing languages

Souganidis

Molinaro

Stoehr

2022

LAB

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It is well established that early bilinguals who speak languages that differ in the phonetic implementation of the voicing contrast have language-specific voicing systems. This study investigates voicing separation in bilinguals who speak two true-voicing languages, Basque and Spanish. We also describe the voicing system in Gipuzkoan Spanish and Gipuzkoan Basque, which is the closest dialect to Standard Basque and it has not yet been investigated experimentally. Twenty Basque-Spanish early bilingual speakers of Gipuzkoan dialects participated in two picture naming tasks. We described their voicing system by measuring voice onset time (VOT) in both Gipuzkoan Basque and Spanish, and used linear mixed-effects models to investigate between-language production differences. Our results show for the first time that adult early bilinguals who speak two true-voicing languages produce language-specific VOT in ‘voiced’ plosives. This finding demonstrates that bilinguals’ phonetic systems during production are more fine-grained than previously assumed, and contributes to a deeper understanding of granularity in early bilingual phonetic systems.

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“…This speech enhancement algorithm is based on a minimum variance estimation criterion to remove noise using a minimum mean square error adaptive filter, where one microphone is used as the reference microphone to receive the noise-containing signal and the other microphones receive only the noise, using the noise as the target signal for the adaptive filter to estimate the source signal [7]. Based on the minimum mean square error criterion to estimate the speech source spectral amplitude and phase, this algorithm assumes that the signal conforms to the Rayleigh distribution and the noise conforms to the Gaussian distribution, which in turn estimates the speech source short-time amplitude spectrum, logarithmic amplitude spectrum, and phase spectrum using the statistical model and the minimum mean square error criterion [8]. Two steps are typically followed in the training of speaking assessment algorithms, namely, score generation and score calibration.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Intelligent Acoustic Sensor-Assisted English Pronunciation Signal Acquisition and Phonetic Calibration

2022

Journal of Sensors

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In this paper, a multimodal intelligent acoustic sensor is used for an in-depth study and analysis of English pronunciation signal acquisition and calibration analysis of English phonetic symbols based on the acquired sound signals. This paper proposes a bimodal fusion algorithm around the direction of feature extension and fusion of acoustic recognition features. After each unimodal classification error cost is minimized, the current fusion process is determined by adaptive weights to fix its one decision layer on the fusion. The adaptive weight approach in this algorithm improves the drawback of always identifying one mode as the optimal mode in fixed-weight fusion and further improves applicability and performance compared to unimodal recognition. The random network generation algorithm is used to generate a random network for sound source data acquisition; then, the algorithm is investigated using the decomposition containing fusion center algorithm to each node, and data preprocessing is implemented at each node; finally, the distributed consistency algorithm based on average weights is used for consistent averaging iterations to achieve a consistent speech enhancement effect at each node. The experimental results show that this distributed algorithm can effectively suppress the interference of noncoherent noise, and each node can obtain an enhanced signal close to the source signal-to-noise ratio. In this study, factors that may affect the readability of spoken texts are summarized, analyzed, defined, and extracted, and the difficulty of spoken items obtained from the divisional scoring model is used as the dependent variable, and the extracted influencing factors are used as independent variables for feature screening, model construction, and tuning, and the generated results are interpreted and analyzed. From this, it was found that phonological features have a strong influence on the readability of spoken texts, mainly in features such as phonemes, syllables, and stress. This study is summarized, and the shortcomings of location-based contextual mobile learning of spoken English in terms of student management, device deployment, and empirical evidence are pointed out, to provide references and lessons for the research on IT-supported language learning.

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Language attitudes modulate phonetic interactions between languages in bilingual speakers in diglossic settings

Cited by 5 publications

References 67 publications

The Effect of Instructed Second Language Learning on the Acoustic Properties of First Language Speech

The Effect of Instructed Second Language Learning on the Acoustic Properties of First Language Speech

Bilinguals produce language-specific voice onset time in two true-voicing languages

Multimodal Intelligent Acoustic Sensor-Assisted English Pronunciation Signal Acquisition and Phonetic Calibration

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