Asymmetrical cognitive load Imposed by processing native and non-native speech

Intonation affects information processing and comprehension. Previous research has found that some international teaching assistants (ITAs) fail to exploit English intonation, potentially posing processing difficulties to students who are native English speakers. However, researchers have also found that non-native listeners found it easier to process sentences given by a non-native speaker with a shared language background, leading to an interlanguage speech intelligibility benefit (ISIB). Therefore, how native speaker teaching assistant (NSTA)’s and ITA’s classroom speech affects the processing, comprehension, and attitudes of listeners with different language backgrounds needs to be further investigated. Using a dual-task paradigm, a comprehension questionnaire, and an attitudinal questionnaire, the present study investigates how the pronunciation and intonation of a NSTA and an ITA affect native English speakers’ and Mandarin-speaking English learners’ processing and comprehension of a lecture, and attitudes towards the two instructors. The present study found shared processing advantages when the listeners shared the L1 of the speaker, but overall lecture comprehension and attitude were unaffected. These findings support and extend prior research studies surveying ITAs’ intonational patterns and ISIB. These findings also have implications for research on the teaching of English pronunciation to non-native instructors.Published versio

Performance Disparities Between Accents in Automatic Speech Recognition

Automatic speech recognition (ASR) services are ubiquitous, transforming speech into text for systems like Amazon's Alexa, Google's Assistant, and Microsoft's Cortana. However, researchers have identified biases in ASR performance between particular English language accents by racial group and by nationality. In this paper, we expand this discussion both qualitatively by relating it to historical precedent and quantitatively through a large-scale audit. Standardization of language and the use of language to maintain global and political power have played an important role in history, which we explain to show the parallels in the ways in which ASR services act on English language speakers today. Then, using a large and global data set of speech from The Speech Accent Archive which includes over 2,700 speakers of English born in 171 different countries, we perform an international audit of some of the most popular English ASR services. We show that performance disparities exist as a function of whether or not a speaker's first language is English and, even when controlling for multiple linguistic covariates, that these disparities have a statistically significant relationship to the political alignment of the speaker's birth country with respect to the United States' geopolitical power

IMPROVING STUDENTS’ PRONUNCIATION SKILL USING ELSA SPEAK APPLICATION

The purpose of this research is to Introduce the ELSA Speak Application as media to improve students' pronunciation skills. English Language Speech Assistant is an acronym for ELSA. English Language Speech Assistant can be downloaded for free from AppStore or Google Play. complete with various features to improve the pronunciation and speaking of students with American accents by training them with various exercises to pronounce words/phrases/sentences correctly. Corresponding to the microphone icon that the learner can use directly to speak as the audio has been listened to.This research is an Action Research that aims to Improve students’ pronunciation skills using ELSA Speak Application. This research discusses how ELSA Speak Application as learning media can improve students’ pronunciation skills. The data were collected through a test of pronunciation and Interviews in Classroom action research. The researcher made three cycles and gave a score for each cycle. In the first cycle, the students’ Average score is 70 points, the second cycle is 75 points and the last cycle is 80 points. ELSA Speak application helps students pronounce a variety of words more easily and comprehensively. The results showed that the use of the ELSA Speak Application has provided convenience and benefits for students in improving their English Pronunciation skills by using the ELSA Speak application

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

Heutagogical Approach in Online English Pronunciation Learning: Student Awareness Survey

English language learners have difficulties in English in pronouncing fricative consonants, voiced and voiceless TH, plosive consonants and vowel sounds. Serious speech mistakes can lead to miscommunication since poor pronunciation abilities make it difficult for other people to hear what you are saying. Additionally, students may miss their lecturers' direct explanations of proper pronunciation when taking online English lessons with inconsistent Internet access. Proper pronunciation can help students participate in conversations, form connections, and improve graduates' employability abilities. Furthermore, pronunciation skills are learnt independently at higher education institutions. Before designing a web-based tool for self-directed learning of English pronunciation, the designers must get feedback from the students about their self-directed learning, self-evaluation of English language proficiency and English learning experiences through Online Distance Learning (ODL). This survey is a preliminary survey towards the development of a heutagogical English pronunciation application. An online questionnaire was disseminated to 424 respondents from various higher education institutions in Malaysia.  In this survey, firstly we aimed to get feedback from the students about their self-directed learning, self-evaluation of English language proficiency and English learning experiences through Online Distance Learning (ODL). The survey consisted of demographic data of respondents, standardized test of English language proficiency, self-evaluation of English language proficiency and English language learning experiences during COVID-19. The findings suggested that although language learners have developed their self-directed learning skills, they still need to improve these skills in learning the language using ODL. Learners consider themselves as having average English language proficiency. They were responsible for improving their pronunciation skills and needed assistant tools to improve their pronunciation skills. It is recommended that an application that uses a heutagogical approach such as Speech-to-Text and Text-to Speech need to be developed

Comparative Study of Intelligent Personal Assistant

When Apple, Inc. launched iPhone 4S on late 2011, one of its point of interest is the new built-in personal assistant application called Siri (Speech Interpretation and Recognition Interface). The application has the ability to answer questions, make recommendations, and perform actions as a response for its user’s voice input using natural language processing. Siri gets favorable reception though it still has some shortcomings such as limited functionality when used outside United States and trouble understanding English with accent. Currently, Siri is only available for iPhone 4s. While there is a chance that Apple’s other mobile device (older iPhone, iPad and iPod) will get Siri in the future, it is very unlikely that it will be opened to another mobile operating system. Since Google’s Android is holding more than half smartphone market in the world as for 2011, we feel the need to find similar application to Siri for Android devices. There are several personal assistant applications using natural language processing available in Google’s Play Store. Top three of the application will be compared in this paper to assess their ability as personal assistant, as same as like Siri for iPhone 4S. They are Iris, Skyvi, and Speaktoit’s Assistant as they have the highest install count, 5 stars rating, and average rating in Play Store. These applications will be tested for its features for input and output correctness, ability to understand questions or command outside their embedded input structure. Some tests will be embedded to provide user scientific reference to determine the best application similar to iPhone’s Siri for Android devices. Based on the test’s results, Speaktoit’s Assistant comes in the first place. It managed to pass all tests with 100% score, beating Skyvi and Iris with 70% and 63.33% score respectively. But still, all the tested applications have much homework to be able to compete with Sir

Continuous Authentication for Voice Assistants

Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the ongoing trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Siri, Google Now and Cortana, have become our everyday fixtures, especially in scenarios where touch interfaces are inconvenient or even dangerous to use, such as driving or exercising. Nevertheless, the open nature of the voice channel makes voice assistants difficult to secure and exposed to various attacks as demonstrated by security researchers. In this paper, we present VAuth, the first system that provides continuous and usable authentication for voice assistants. We design VAuth to fit in various widely-adopted wearable devices, such as eyeglasses, earphones/buds and necklaces, where it collects the body-surface vibrations of the user and matches it with the speech signal received by the voice assistant's microphone. VAuth guarantees that the voice assistant executes only the commands that originate from the voice of the owner. We have evaluated VAuth with 18 users and 30 voice commands and find it to achieve an almost perfect matching accuracy with less than 0.1% false positive rate, regardless of VAuth's position on the body and the user's language, accent or mobility. VAuth successfully thwarts different practical attacks, such as replayed attacks, mangled voice attacks, or impersonation attacks. It also has low energy and latency overheads and is compatible with most existing voice assistants