The major aim of the current study is to verify whether an interdependence between self-imitation practice and L2 pronunciation improvement in the process of second-language acquisition is stronger than traditional imitation tasks. 35 Polish students of Applied Linguistics (at English level B2+) divided into two groups performed imitation and self-imitation exercises in order to improve their L2 pronunciation skills. Three acoustic parameters were considered, namely, articulation rate, speech rate and average syllable duration. The results of the research have revealed that there is a significant interdependence between L2 pronunciation improvement and self-imitation training in terms of speech rate. The outcomes of the research are in line with Ding et al. (2019), De Meo et al.’s (2013), and Felps et al.’s (2009) assertion that the better the match between learners’ voices and their modified equivalents, the more positive impact there is on L2 pronunciation training.
The present study describes the level of effectiveness of both traditional and computer-assisted second language pronunciation techniques from the students’ perspectives. By traditional techniques we mean those activities which make use of phonetic alphabet, including transcription practice, detailed description of the articulatory systems, drills (e.g. minimal pair drills), reading aloud, tongue twisters, rhymes, etc. (Hismanoglu and Hismanoglu 2010: 985). On the other hand, computer-assisted techniques include activities based on listening and imitating tasks, which use technology, such as self-imitation practice, recordings of L2 learner’s, visual aids, and automatic speech recognition tools. The main aim of this study does not aim to classify L2 pronunciation methods by allocating them to previously mentioned categories but rather attempts to examine the intricate relationship between students’ knowledge, perceptions, attitudes and their most preferable practices which, in their opinion, result in improvement of their L2 pronunciation. 118 study subjects were asked to complete four main questions, within which tasks based on the Likert-scale items gathered data about the students’ most preferable L2 pronunciation teaching and learning techniques. The students were asked to create their own list, starting from the most useful to the least beneficial techniques. The last task was an open-ended question about other techniques than mentioned in the questionnaire. The analysis of the obtained data involved a two-stage process: a) data segmentation; and b) techniques categorisation. The first step was to select pronunciation learning techniques in terms of their frequency and use and to adjust them to the research group. The second stage, techniques categorisation, was based on a careful analysis of the answers given by the students in the questionnaire. Following that, five categories were distinguished: (1) traditional and used only in the classroom, (2) traditional but also used in distance learning, (3) computer-assisted but used only in the classroom, (4) computer-assisted and also used in distance learning, (5) innovative: combining students’ needs and available online.Highlighting the prominence of pronunciation in acquiring communicative competence, the authors propose their own, innovative suggestions for the future creation of teaching materials.
The major aim of this paper is to emphasise the importance of implementing statistical tools in the field of linguistic research, as well as to acquaint the reader with the basic statistical methods that can be used while conducting linguistic studies. The article introduces the idea of five steps in data analysis that any researcher of applied linguistics can take in order to carry out relevant studies. The steps include choosing statistical programmes, eliciting data, selecting some visual methods and applying normality tests, as well as choosing applicable parametric or nonparametric tests, all of which requires appropriate planning, designing, analysing and interpreting data. The theoretical part is an interlude to the practical realisation of the above-mentioned five steps, which is based on the part of linguistic research conducted on the students of English Philology. The major purpose of it was to prove (or refute) that there is a positive correlation between participants’ level of musical intelligence and their L2 pronunciation skills. The practical use of statistical methods enables the readers to familiarise themselves with one of the patterns of statistical analysis in the field of applied linguistics.
One innovative method of L2 pronunciation improvement is self-imitation practice, which involves mirroring recordings of one's own voice after they have been resynthesized to match native speaker pronunciations of target sounds. The Golden Speaker Builder (GSB) (Ding et al., 2019) is a tool that allows users to generate such a personalised model voice, mirroring the learner's voice quality, but with a native accent. In this study we investigate the effects of using the GSB and to what extent it affects L2 learners' comprehensibility and fluency. Thirty-five participants in the study performed a three-week self-imitation task by repeating some of the sentences they had previously recorded. The participants took a pre-test, a post-test after three weeks of practice, and a delayed post-test. Each participant completed two qualitative questionnaires before and after the exercises, to guage their opinion about the tool used. The results show a significant improvement in pronunciation in terms of comprehensibility and fluency, but the feedback from the questionnaire indicates that GSB cannot replace the personalised comments received directly from a teacher during pronunciation training.
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