The Implementation of Automated Speech Recognition (ASR) in ELT Classroom: A Systematic Literature Review from 2012-2023

Automated Speech Recognition (ASR) turns speech audio streams into text. The use of computer-based voice recognition is beneficial for teaching pronunciation. It may also be used to evaluate a learner's speech in a broader context and set up the potential for creating aural interactions between the learner and the computer. The study is aimed to investigate the use of ASR in speaking assessment using a systematic literature review. Automated scores should be considered based on their validity and any potential issues or mistakes with employing technology (ASR) to evaluate speaking. Although some research has been carried out on the use of technology in speaking assessment, there have been few empirical studies using systematic literature reviews to explore more about ASR in education. Therefore, this study is aimed to provide an updated and comprehensive review of the implementation of Automated Speech Recognition (ASR) in education. Ten studies from journals cited in the Taylor Francis, Wiley, and Springer databases were selected.  The results show the benefits of ASR in education including students' progress, interaction, and pedagogical contributions. Pedagogical contributions provide collaboration between the human and automated scores. Teachers can use this study to improve speaking assessments using ASR in the classroom

Second language pronunciation assessment: A look at the present and the future

Over three decades ago, Michael Canale summarized what he considered to be the challenges facing language assessment in the era of communicative language learning and teaching: Just as the shift in emphasis from language form to language use has placed new demands on language teaching, so too has it placed new demands on language testing. Evaluation within a communicative approach must address, for example, new content areas such as sociolin- guistic appropriateness rules, new testing formats to permit and encour- age creative, open-ended language use, new test administration procedures to emphasize interpersonal interaction in authentic situa- tions, and new scoring procedures of a manual and judgemental nature. (Canale, 1984: 79) Applied to second language (L2) pronunciation assessment, this descrip- tion remains highly relevant today, raising a number of important issues, such as: broadening the scope of pronunciation assessment beyond the focus on a single aspect of pronunciation (e.g. segmental accuracy) or a single standard (e.g. absence of a discernible nonnative accent); targeting pronunciation assess- ment for various interlocutors in interactive settings, for instance, outside a typical focus on academic performance by students in Western societies; as well as developing and fine-tuning novel assessment instruments and proce- dures. Above all, Canale’s description aptly summarizes an ongoing quest in language assessment to capture the authenticity and interactiveness of language use (e.g. Bachman, 1990; Bachman & Palmer, 2010). The contribu- tions to this edited volume address some of the challenges identified by Canale in innovative ways. Before summarizing these contributions, we hasten to add that no edited volume, including this one, can provide an exhaustive overview of all possible issues in L2 pronunciation assessment; most chapters in this volume are focused on testing or informal evaluative judgements of speech in real-world settings and not on classroom-based assessment, including diagnostic assessment or feedback on test takers’ per- formance. However, the range of topics, the variety of research methodologies and paradigms, and the scope of implications featured here make this volume a timely addition to the growing area of L2 pronunciation assessment

Comparación de dos métodos basados en la intensidad para el cálculo automático de la velocidad de habla

Automatic computation of speech rate is a necessary task in a wide range of applications that require this prosodic feature, in which a manual transcription and time alignments are not available. Several tools have been developed to this end, but not enough research has been conducted yet to see to what extent they are scalable to other languages. In the present work, we take two off-the- shelf tools designed for automatic speech rate computation and already tested for Dutch and English (v1, which relies on intensity peaks preceded by an intensity dip to find syllable nuclei and v3, which relies on intensity peaks surrounded by dips) and we apply them to read and spontaneous Spanish speech. Then, we test which of them offers the best performance. The results obtained with precision and normalized mean squared error metrics showed that v3 performs better than v1. However, recall measurement shows a better performance of v1, which suggests that a more fine-grained analysis on sensitivity and specificity is needed to select the best option depending on the application we are dealing with.El cálculo automático de la velocidad de habla es una tarea fonética útil y que además se hace indispensable cuando no hay disponible una transcripción manual a partir de la cual determinar una tasa de habla manual. Se han desarrollado varias herramientas para este fin, pero todavía no se ha llevado a cabo suficiente investigación para ver hasta qué punto las herramientas son aplicables a lenguas distintas para las que fueron diseñadas. En este artículo probamos dos herramientas para el cálculo automático de la velocidad de habla ya evaluadas para el neerlandés y el inglés (v1, que se basa en la determinación de picos de intensidad precedidos de un valle para encontrar núcleos de sílaba, y v3, que se basa en picos de intensidad rodeados de valles) y las aplicamos a un corpus de habla leída y espontánea del español para analizar cuál ofrece mejores resultados en español. Los resultados de precisión y del error cuadrático mediano normalizado obtenidos muestran que v3 funciona mejor que v1. No obstante, el recall muestra mejor rendimiento para la v1, lo que nos indica que se necesita un análisis detallado de la sensibilidad y la especificidad para seleccionar la mejor opción en función de los objetivos del análisis posterior que se quiera hacer

Automatic Pronunciation Assessment of Korean Spoken by L2 Learners Using Best Feature Set Selection

This paper proposes a method for automatic pronunciation assessment of Korean spoken by L2 learners by selecting the best feature set from a collection of the most well-known features in the literature. The L2 Korean Speech Corpus is used for assessment modeling, where the native languages of the L2 learners are English, Chinese, Japanese, Russian, and Mongolian. In our system, learners speech is forced-aligned and recognized using a native Korean acoustic model. Based on these results, various features for pronunciation assessment are computed, and divided into four categories such as RATE, SEGMENT, SILENCE, and GOP. Pronunciation scores produced by combining categories of features by multiple linear regression are used as a baseline. In order to enhance the baseline performance, relevant features are selected by using Principal Component Regression (PCR) and Best Subset Selection (BSS), respectively. The results show that the BSS model outperforms the baseline and the PCR model, and that features corresponding to speech segment and rate are selected as the relevant ones for automatic pronunciation assessment. The observed tendency of salient features will be useful for further improvement of automatic pronunciation assessment model for Korean language learners.OAIID:RECH_ACHV_DSTSH_NO:A201625650RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A076305CITE_RATE:FILENAME:2016_09 (APSIPA 류혁수).pdfDEPT_NM:언어학과EMAIL:[email protected]_YN:FILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/9614f371-16ac-45af-add0-9434be5bacf0/linkCONFIRM:

20 years of technology and language assessment

This review article provides an analysis of the research from the last two decades on the theme of technology and second language assessment. Based on an examination of the assessment scholarship published in Language Learning & Technology since its launch in 1997, we analyzed the review articles, research articles, book reviews, and commentaries as developing one of two primary thrusts of research on technology and language assessment: technology for efficiency and technology for innovation