Elsa Speak App: Automatic Speech Recognition (ASR) for Supplementing English Pronunciation Skills

Nowadays, artificial intelligence (AI) became a special concern in language teaching for the reason that it can assist and enhance language learning for all levels of education. Again, it had beneficial roles for supplementing language teaching like ELSA Speak App one of Automatic Speech Recognition (ASR) used for teaching pronunciation. It studied how students heard, voiced, uttered, vocalized, and asserted the English words in the oral language, but the students often pronounced incorrect words with the result that the uttered words had faulty meaning. This study aimed to carry out English Language Speech Assistant (ELSA) Speak App to improve English language pronunciation skills to higher education learners that were the English Department Students of Nahdlatul Ulama University of Yogyakarta (UNU). The data were collected using a test of pronunciation and interview. The researcher also taught in the classroom. The results showed that ELSA Speak can increase the students’ pronunciation skills. It can be seen from the average scores obtained from the teaching cycles from two to four in grade. Clearly, ELSA Speak helped the students pronounce diverse words more easily and comprehensively. Also, the available features offered by this app like instant feedback enabled the students to pronounce precisely. In conclusion, ELSA Speak can improve the students’ pronunciation skills well and effectively. Indeed, it can motivate the students to engage in learning to pronounce

Asr-dictation on smartphones for vowel pronunciation practice

This study aims to explore mobile-assisted Automated Speech Recognition (ASR) dictation systems for vowel pronunciation practice by examining whether ARS can be useful for pronunciation improvement and speech recognition accuracy. Additionally, learners’ attitudes towards using these systems were explored. Twenty-one Macedonian EFL learners practiced pronouncing 26 words with the following minimal pairs: /i/, /ɪ/; /æ/, /ɛ/; /u/, /ʊ/; /ɑ/, /ʌ/. The participants were divided into an experimental group (n=11) and a control group (n=10). This study used a mixed methods approach including qualitative and quantitative analysis. Results demonstrated that while the control group did not show any improvement, the experimental group improved their accuracy. ASR written output and human judgment was also found to be within an acceptable agreement for most vowels. Furthermore, while occasional inaccurate feedback sometimes caused frustration, ASR training was generally enjoyed and considered as a practical and safe environment for practice. The findings provide some support for the use of ASR in EFL classrooms with careful planning and direction from the teacher. Using ASR as a tool for controlled and structured practice with individual words is particularly applicable when the focus is to raise learners’ phonological awareness and perception of English vowel sounds

Una aproximación del efecto en el aprendizaje de una lengua extranjera debida a la obtención de datos a través de exámenes en línea de idiomas

Artificial intelligence oriented to education (AIEd) allows the adequacy and / or adaption to the user’s learning itineraries through inductive processes based on the extraction of data obtained from the formative evidences that it generates throughout its school life. Big data, or massive data, is the storage of large amounts of data that can be analyzed by various procedures and allows us to find repetitive patterns or predictive formulas that can generate learning about ourselves and especially the network. In the case of the massive data that are generated through the use of tests in the learning and certification of knowledge of languages as a foreign language at the national level, we find that it might be useful to apply Big Data's processing methodologies in order to know better if the information Generated through the tests can improve or create new learning strategies or establish formal criteria in the design of the tests, theories of second language acquisition, or even educational policies. The novelty of the article focuses on establishing viable guidelines to apply the more generic concepts of Big Data in the specific context of the tests of language evaluation as a second language and where there is a priori a large amount of information to be processed at the educational level. The article shows some guidelines that could be applied in the mechanisms used in the extraction of educational data from large-scale language learning in the specific environment of language assessment tests as a foreign language.La Inteligencia Artificial orientada a la educación (AIEd) permite adecuar y/o adaptar los itinerarios del aprendizaje de un usuario mediante procesos inductivos basados en la extracción de datos obtenidos de las evidencias formativas que genera a lo largo de su vida escolar. El Big data, o datos masivos es el almacenamiento de grandes cantidades de datos que pueden ser analizados por diversos procedimientos y que permite encontrar patrones repetitivos o formulas predictivas que pueden generar un aprendizaje sobre nosotros mismos y sobre todo en la red. En el caso de los datos masivos que se generan a través de los exámenes utilizados en el aprendizaje y certificación de conocimiento de idiomas como segunda lengua a nivel nacional encontramos que podría ser útil aplicar las metodologías de procesamiento del Big Data para conocer mejor si la información generada a través de los test pueden mejorar o crear nuevas estrategias de aprendizaje o establecer criterios formales en el diseño de las pruebas, teorías de adquisición de se segunda lengua o incluso políticas educativas. La novedad de artículo se centra en establecer directrices viables para aplicar los conceptos más genéricos del Big Data en el contexto específico de los test de evaluación de idiomas como segunda lengua y donde existe a priori una gran cantidad de información a procesar a nivel educativo. El artículo muestra algunas directrices que podrían aplicarse en los mecanismos aplicados en la extracción de datos educativos del aprendizaje de idiomas a gran escala en el entorno específico de los test de evaluación de idiomas como lengua extranjera

Promoting Increased Pitch Variation in Oral Presentations with Transient Visual Feedback

This paper investigates learner response to a novel kind of intonation feedback generated from speech analysis. Instead of displays of pitch curves, the feedback our system produces is flashing lights of different colors, which show how much pitch variation the speaker has produced rather than an absolute measure of frequency. The variable used to generate the feedback is the standard deviation of fundamental frequency (as measured in semitones) over the previous ten seconds of speech. Flat or monotone speech causes the system to show yellow lights, while more expressive speech that has used pitch to give focus to any part of an utterance generates green lights. The system is designed to be used with free, rather than modeled, speech. Participants in the study were 14 Chinese-native students of English at intermediate and advanced levels. A group that received feedback was compared with a group that received no feedback other than the ability to listen to recordings of their speech, with the hypothesis that the feedback would stimulate the development of a speaking style that used more pitch variation. Pitch variation was measured at four stages of our study: in a baseline oral presentation; for the first and second halves of roughly three hours of training; and finally in the production of a new oral presentation. Both groups increased their pitch variation with training, and the effect lasted after the training had ended. The test group showed a significantly higher increase than the control group, indicating that the feedback is effective. These positive results imply that the feedback could be beneficially used in a system for practicing oral presentations

Overcoming segmental difficulties in English pronunciation in Spanish 3-ESO bilingual students through the use of SpeechAce

The aim of this study is to account for the main problems with which secondary school students are faced in terms of English pronunciation, and to look into the effectiveness of ‘SpeechAce’ as a tool for English phoneme pronunciation improvement. Specifically, the data obtained were evaluated considering the challenging English phonemes, the mispronunciation rate of each English phoneme prior to the use of ‘SpeechAce’, and the improvement rate shown upon its use. The results obtained show that both consonant and vowel sounds present similar mispronunciation rates before the use of ‘SpeechAce’ as well as similar improvement rates after its use. Thereby, ‘SpeechAce’ has proved to be a useful tool to overcome secondary school students’ segmental difficulties in English pronunciation.El objetivo de este Trabajo de Fin de Máster consiste en explicar los principales problemas de pronunciación inglesa que presentan los alumnos de Educación Secundaria Obligatoria e investigar la efectividad de ‘SpeechAce’ como herramienta para solventarlos. En concreto, se han analizado los fonemas ingleses más complicados, la tasa de pronunciación incorrecta de cada fonema antes de utilizar ‘SpeechAce’, y la tasa de mejora después de su uso. En términos generales, los resultados del estudio demuestran que los sonidos consonánticos y vocálicos presentan tasas similares de pronunciación incorrecta antes del uso de ‘SpeechAce’, además de tasas de mejora semejantes después de su uso. Por lo tanto, ‘SpeechAce’ ha demostrado ser una herramienta de utilidad a la hora de solucionar los problemas relacionados con los elementos segmentales de la pronunciación inglesa.Departamento de Filología InglesaMáster en Profesor de Educación Secundaria Obligatoria y Bachillerato, Formación Profesional y Enseñanzas de Idioma

QUÃO BEM A TECNOLOGIA RAF PODE ENTENDER A FALA COM SOTAQUE ESTRANGEIRO?

Following the Covid-19 pandemic, digital technology is more present in classrooms than ever. Automatic Speech Recognition (ASR) offers interesting possibilities for language learners to produce more output in a foreign language (FL). ASR is especially suited for autonomous pronunciation learning when used as a dictation tool that transcribes the learner’s speech (McCROCKLIN, 2016). However, ASR tools are trained with monolingual native speakers in mind, not reflecting the global reality of English speakers. Consequently, the present study examined how well two ASR-based dictation tools understand foreign-accented speech, and which FL speech features cause intelligibility breakdowns. English speech samples of 15 Brazilian Portuguese and 15 Spanish speakers were obtained from an online database (WEINBERGER, 2015) and submitted to two ASR dictation tools: Microsoft Word and VoiceNotebook. The resulting transcriptions were manually inspected, coded and categorized. The results show that overall intelligibility was high for both tools. However, many features of normal FL speech, such as vowel and consonant substitution, caused the ASR dictation tools to misinterpret the message leading to communication breakdowns. The results are discussed from a pedagogical viewpoint.Após a pandemia de Covid-19, as tecnologias digitais estão mais presente nas salas de aula do que nunca. O Reconhecimento Automático da Fala (RAF) oferece possibilidades interessantes para os aprendizes de uma língua estrangeira (LE) aumentarem sua produção oral. O RAF é especialmente adequado para a aprendizagem autônoma de pronúncia quando usado como uma ferramenta de ditado que transcreve a fala do estudante (McCROCKLIN, 2016). No entanto, as ferramentas de RAF são treinadas com falantes nativos monolíngues em mente, não refletindo a realidade dos falantes de inglês em uma escala global. Consequentemente, o presente estudo examinou quão bem duas ferramentas de ditado que utilizam ASR entendem a fala com sotaque estrangeiro e quais características causam falhas de inteligibilidade. Amostras de fala em inglês de 15 falantes de português brasileiro e 15 falantes de espanhol foram obtidas de um banco de dados online (WEINBERGER, 2015) e submetidas a duas ferramentas de ASR: Microsoft Word e VoiceNotebook. As transcrições foram manualmente inspecionadas, codificadas e categorizadas. Os resultados mostram que a inteligibilidade geral dos falantes foi alta para ambas as ferramentas. No entanto, muitas características normais, como modificações vocálicas e consonantais, da fala em LE fizeram com que as ferramentas de ditado ASR interpretassem mal a mensagem, levando a falhas de comunicação. Os resultados são discutidos do ponto de vista pedagógico

Teaching suprasegmentals through strategy use and technology

textIn an increasingly global world, there is a growing demand for pronunciation instruction. A focus on intelligibility as the goal in pronunciation instruction has risen to the forefront of the literature. Suprasegmentals are a practical goal for instruction since they have been shown to have a greater impact on overall intelligibility, but many language curricula do not allocate time or resources to pronunciation instruction at all, making it difficult for teachers to incorporate much needed suprasegmental learning in language classrooms. This Report suggests a practical way to incorporate suprasegmental learning in a mixed language background classroom: a project that incorporates a Web 2.0 tool, VoiceThread, and Covert Rehearsal Model strategy use.Foreign Language Educatio

Methods for second language training of adult immigrants: a systematic scoping review

Source at https://www.fhi.no/.Vi utførte på oppdrag fra Integrerings- og mangfoldsdirektoratet (IMDi) en systematisk kartleggingsoversikt. Den sammenfattet studier om undervisningsstrategier for andrespråksopplæring av voksne innvandrere. Vi utførte et svært omfattende søk, og vurderte ca. 1100 studier i fulltekst. Vi inkluderte 66 studier, med totalt ca. 79 000 deltakere i studiene (77 060 deltakere er fra én svensk registerbasert studie). Det var flest kvalitative studier, men vi inkluderte også 12 kontrollerte studier. Tre fjerdedeler av studiene er publisert det siste tiåret. Majoriteten av studiene er fra Europa, inkludert 21 studier fra Skandinavia

ASR as a tool for providing feedback for vowel pronunciation practice

The purpose of the study is to examine the usefulness of mobile-assisted ASR dictation systems (Gboard, Siri or voice dictation on smartphones) for vowel pronunciation practice by looking at three aspects of its usefulness: pronunciation improvement by using ASR, accuracy of recognition, and the learners’ attitudes towards using this system. A list of 30 words containing minimal pairs of the contrasts /i/, /ɪ/; /æ/, /ɛ/; /u/, /ʊ/; /ɑ/, /ʌ/ and some distractors was given to 21 Macedonian EFL learners, divided into two groups, an experimental (n=11) and a control group (n=10). A mixed methods approach was used in this study. The quantitative part of the study included pre-test and post-test recordings which were transcribed by 10 native listeners to measure their accuracy gains, as well as a comparison between ASR written output of native speakers and that of non-native speakers, and another comparison between ASR written output of non-native speakers and human judgments. The qualitative analysis explored learners’ attitudes towards ASR by analyzing students’ Facebook posts throughout the practice period. Findings showed that the experimental group improved their accuracy while the control group did not show any improvements. Next, the findings demonstrated a close relationship between ASR written output and human judgment with an acceptable agreement for most of the vowels. Nonetheless, ASR did not show high recognition of native speech, especially for the vowels /ʊ/ and /ʌ/. Qualitatively, the learners’ Facebook posts showed positive attitudes towards ASR. An occasional frustration with inaccurate feedback was also reported but learners generally enjoyed the training and found ASR to be practical and a safe environment for practice. This study recommends inclusion of mobile-assisted ASR in the EFL classrooms for raising students’ awareness of the vowel sounds in the English language with careful guidance from the teacher as well as focused and structured practice using individual words

Online Translators: Can They Help English Learners improve their pronunciation?

The literature reports a number of limitations that affect the teaching and learning of foreign languages, including a lack of teacher preparedness (Hu, 2005) and insufficient time for practice (Life, 2011). To mitigate these challenges, we proposed a self-directed learning (SDL) environment assisted by a combination of text-to-speech synthesis (TTS) and automatic speech recognition (ASR) technologies, as found in Microsoft Translator (MT), to examine whether this translation tool and its built-in speech features can promote the acquisition in pronunciation of English regular past tense -ed in a self-directed manner. This study followed a pretest/posttest research design in which participants received autonomous but teacher-assisted TTS- and ASR-based treatment to learn about the pronunciation of English past -ed allomorphy: this suffix can be pronounced as play/d/, visit/ɪd/ and walk/t/, depending on the preceding phonological environment. We compared 29 participants’ performance in past -ed allomorphy by assessing their phonological development in terms of phonological awareness, phonemic discrimination, and oral production, as per Celce-Murcia et al.’s (2010) framework for pronunciation instruction. The t-test results showed that there were significant improvements in participants’ phonological awareness and oral production of English past -ed allomorphy. For the phonemic discrimination tests, the results were inconclusive: the participants only improved in recognizing the /t/ allomorph. These findings highlight the affordances of MT and its speech capabilities regarding its pedagogical use for improving second language learners’ pronunciation