Podcast-based pronunciation training: Enhancing FL learners’ perception and production of fossilised segmental features

Fouz-González, Jonás

doi:10.1017/s0958344018000174

Cited by 37 publications

(22 citation statements)

References 42 publications

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“…Finally, as long as learners have accurate concepts for the features notation represents, notation can also be used to offer learners feedback on their performance. For example, in Fouz-González’s (2019) study with podcasts, learners were asked to listen to each other’s recordings and offer peer feedback on their productions. To evaluate the learners’ pronunciation of English /z/ – which tends to be pronounced as Spanish /s/ – rather than asking learners to describe participants’ problems, feedback was simplified by asking them to mark particular examples of their peer recordings as /s/ or /z/.…”

Section: Using Phonetic Notation In Contemporary Pronunciation Instrumentioning

confidence: 99%

Phonetic Symbols in Contemporary Pronunciation Instruction

Mompean

Fouz-González

2020

RELC Journal

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This article explores the potential of phonetic symbols in pronunciation teaching/learning, with a focus on English language teaching (ELT). After a brief contextualisation of current views of pronunciation instruction in the second-language (L2) teaching literature, the paper reviews some of the potential advantages of, conditions for, and alternatives to their use together with research-based evidence. The exploration then focuses on the use of phonetic symbols for pronunciation instruction in contemporary linguistically superdiverse contexts, marked by shifting teaching goals, as well as accent varieties, ELT learner profiles, learning/teaching environments, and notions of L2 English. The discussion concludes with some suggestions as to how to use phonetic notation in contemporary ELT instruction.

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Section: Using Phonetic Notation In Contemporary Pronunciation Instrumentioning

confidence: 99%

Phonetic Symbols in Contemporary Pronunciation Instruction

Mompean

Fouz-González

2020

RELC Journal

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“…In providing language learners with opportunities to rehearse representations of language form, the use of digital media intermingled with FonF options can be an innovative idea for L2 teachers. Drawing on web‐based podcasting can enhance the standards of instruction as far as teaching certain phonological skills is concerned (see also Fouz‐González, ; Lord, ).…”

Section: Discussionmentioning

confidence: 99%

Focus on form options in second language pronunciation instruction: The case of lexical stress

Hassanzadeh

Salehizadeh

2019

TESOL Journal

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Pronunciation instruction has historically been notorious for its intense reliance on repetition practice and mechanical drilling across most foreign and second language settings.However, research indicates that form-focused instruction can be substantially beneficial to the development of phonological skills (Saito, 2012). The current quasi-experimental study builds on this strand of research by measuring the impact of focus on form (FonF) on English as a foreign language learners' retention of English word stress patterns, drawing mainly upon the so-called macro-options introduced by Ellis (e.g., 2008). Eight intact classes (N = 129), randomly assigned to four groups (output, input-enhancement, corrective feedback, and control) were subjected to a series of mini-lessons orchestrated by the researchers over an 8-week period. The learners' short-and long-term phonological uptake was assessed by immediate and delayed word-reading tests, respectively. The results indicate that the output-oriented group outperformed the others on the immediate posttest, whereas the input-enhancement group outdid the corrective feedback group. However, none of the three FonF options had a significant durable impact. The findings imply that production-based instruction must be given a high priority in the domain of teaching suprasegmental features, not least lexical stress. Furthermore, phonologically overloading learners during each instructional period is not recommended.Error 125

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“…Traditional speaking tests have experts as scorers, where more factors can lead to unfair scoring, such as scorer evaluation concerns, experience, and ability, age, gender, and psychology. Machine-based speaking assessments, on the other hand, exclude a range of subjective factors that can cause problems, thus ensuring that the speaking assessment algorithm or system has high reliability [11]. Most of these systems are based on ubiquitous learning theory and generally push location-related content information to learners based on their different location information [12].…”

Section: Related Workmentioning

confidence: 99%

“…jμ i Þ is the probability density function of one of the Gaussian components, μ i denotes the mean vector of the Gaussian components, and x ! is the covariance matrix and is calculated in Equation (11).…”

Section: Experimental Design For English Pronunciation Phoneticmentioning

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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Podcast-based pronunciation training: Enhancing FL learners’ perception and production of fossilised segmental features

Cited by 37 publications

References 42 publications

Phonetic Symbols in Contemporary Pronunciation Instruction

Phonetic Symbols in Contemporary Pronunciation Instruction

Focus on form options in second language pronunciation instruction: The case of lexical stress

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

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