603 research outputs found

    Whaddya call that again? Materials for teaching connected speech

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    In order to examine the phenomena of connected speech and the place technology has in its instruction, I must first examine the developments in speaking and listening instructor that have contributed to this area of research, instruction, and learning. The literature review, then, will present (a) an overview of current speaking instruction trends, (b) an overview of current listening instruction trends, (c) an explanation of connected speech and its features, (d) an overview of technology and computer-assisted language learning (CALL), and (e) an overview of technological interventions in connected speech instruction. Through my findings, I hope to explore the following research questions: 1. How do instructors and learners feel about pronunciation, listening, and connected speech instruction? 2. How do instructors and learners feel about using technology to mediate the above instruction? 3. What do instructors and learners think of a number of activities developed in light of RQs 1 and 2? 4. How does the research literature reflect the topics of pronunciation, pronunciation with suprasegmentals, and suprasegmentals with technology? 5. How can a series of pedagogical materials support the technology-mediated instruction of connected speech

    Voice-controlled in-vehicle infotainment system

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    Abstract. Speech is a form of a human to human communication that can convey information in a context-rich way that is natural to humans. The naturalness enables us to speak while doing other things, such as driving a vehicle. With the advancement of computing technologies, more and more personal services are introduced for the in-vehicle environment. A limiting factor for these advancements is the impact they cause towards driver distraction with the increased cognitive stress load. This has led to developing in-vehicle devices and applications with a heightened focus on lessening distraction. Amazon Alexa is a natural language processing system that enables its users to receive information and operate smart devices with their voices. This Master’s thesis aims to demonstrate how Alexa could be utilized when operating the in-vehicle infotainment (IVI) systems. This research was conducted by utilizing the design science research methodology. The feasibility of voice-based interaction was assessed by implementing the system as a demonstrable use-case in collaboration with the APPSTACLE project. Prior research was gathered by conducting a literature review on voice-based interaction and its integration to the vehicular domain. The system was designed by applying existing theories together with the requirements of the application domain. The designed system utilized the Amazon Alexa ecosystem and AWS services to provide the vehicular environment with new functionalities. Access to cloud-based speech processing and decision-making makes it possible to design an extendable speech interface where the driver can carry out secondary tasks by using their voice, such as requesting navigation information. The evaluation was done by comparing the system’s performance against the derived requirements. With the results of the evaluation process, the feasibility of the system could be assessed against the objectives of the study: The resulting artefact enables the user to operate the in-vehicle infotainment system while focusing on a separate task. The research proved that speech interfaces with modern technology can improve the handling of secondary tasks while driving, and the resulting system was operable without introducing additional distractions to the driver. The resulting artefact can be integrated into similar systems and used as a base tool for future research on voice-controlled interfaces

    Speech tools and technologies

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    The Effects of English Pronunciation Instruction on Listening Skills among Vietnamese Learners

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    Listening has been a neglected skill in both second language research and teaching practice (Khaghaninejad & Maleki, 2015; Nowrouzi, Tam, Zareian & Nimehchisalem, 2015) and recent research has shown that second language (L2) listening difficulties might relate to phonological problems besides syntactic and lexical knowledge (e.g., Suristro, 2018). There have been some empirical studies examining the effects of phonetic instruction on perceptual skills showing promising results (e.g., Aliaga-Garcia & Mora, 2009; Linebaugh & Roche, 2013). This study contributes to this area with a focus on investigating the impacts of English pronunciation instruction on listening skills among Vietnamese English as a Foreign Language (EFL) learners, targeting the four English phonemes: word-final stop consonants /t/-/d/, the lax high front vowel /ɪ/ and the tense high front vowel /i/. Particularly, it examines whether pronunciation instruction would have effects on (a) students’ abilities to listen to and distinguish target phonemes, and (b) students’ abilities to listen to and dictate monosyllabic words containing the target sounds. To examine the effects of mere explicit pronunciation instruction on perception, the study excluded perceptual training from the treatment. Sixteen Vietnamese learners were recruited to join the study, divided into two groups: an experimental group (n=10) and a control group (n=6). Only the experimental group received a five-hour online phonetic instruction emphasizing the four English target phonemes and other distractors. A pre-test and a post-test in listening skills measured the difference between and within groups. In addition, a post-instructional survey was administered to collect qualitative data in an attempt to explain the study results. Non-parametric tests (Wilcoxon rank sum and Wilcoxon signed rank tests) were used to analyze the quantitative data. The study results revealed that there was no difference in listening performance between the two groups, and within each group, which might suggest unclear impact of pronunciation instruction on perceptual skills. Perceptual training, which has often been used in research on pronunciation instruction, is discussed and suggestions for future research are made

    Learning second language speech perception in natural settings

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    A prefix encoding for a constructed language

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    This work focuses in the formal and technical analysis of some aspects of a constructed language. As a first part of the work, a possible coding for the language will be studied, emphasizing the pre x coding, for which an extension of the Hu man algorithm from binary to n-ary will be implemented. Because of that in the language we can't know a priori the frequency of use of the words, a study will be done and several strategies will be proposed for an open words system, analyzing previously the existing number of words in current natural languages. As a possible upgrade of the coding, we'll take also a look to the synchronization loss problem, as well as to its solution: the self-synchronization, a t-codes study with the number of possible words for the language, as well as other alternatives. Finally, and from a less formal approach, several applications for the language have been developed: A voice synthesizer, a speech recognition system and a system font for the use of the language in text processors. For each of these applications, the process used for its construction, as well as the problems encountered and still to solve in each will be detailed

    The potential of Automatic Speech Recognition for fostering pronunciation learners\u27 autonomy

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    Despite ESL students frequently reporting a need or desire to work on their pronunciation in English, pronunciation is often downgraded as a teaching goal and often pushed aside in favor of other skills (Kelly, 1969; Lang, Wang, Shen, & Wang, 2012). Students that want to practice outside of class are likely to feel at a loss because they struggle to monitor their own speech and may not be able to get the feedback necessary to make improvements to their pronunciation. Students need skills, strategies, and resources that will allow them to work on their pronunciation on their own, less reliant on a teacher or school for pronunciation training. In effect, students need to learn to become autonomous learners of pronunciation. Automatic Speech Recognition (ASR) has great potential as a technology to help students get feedback on their pronunciation, allowing them to be more autonomous pronunciation learners. This study seeks to examine the effect of ASR on students\u27 autonomous learning beliefs and behaviors. Three groups, a control group (TRAD, n=15) which received traditional face-to-face (F2F) instruction, an experimental group (STRAT, n=17) which received traditional F2F instructions, but also minimal strategy training in ASR, and a second experimental group (HYBRID, n=16) which received hybrid instruction (half F2F with minimal strategy training and half working with ASR) were given a three-week pronunciation workshop on consonants and vowels of English known to be problematic for ESL students. Changes in beliefs of autonomy were measured through pre- and post-workshop surveys with Likert scale items as well as semi-structured interviews. Autonomous learning behaviors were monitored through self-reports of behavior during the course with language learning logs and after the course with a delayed post-workshop survey. Students explained choices to continue or stop working with ASR during a focus group at the end of the study. Results showed that STRAT and HYBRID both significantly increased their beliefs of autonomy from the pre- to post-workshop survey (for STRAT p=.006 and for HYBRID p=.013), while TRAD did not (p=.727). Students primarily pointed to ASR as the reason that they felt more capable of practicing their pronunciation on their own, stating that the ASR was useful for feedback because they could not hear their own errors when speaking. HYBRID reported significantly more time spent on autonomous pronunciation learning than STRAT and TRAD after the pronunciation workshop (p=.011). HYBRID also reported significantly more use of dictation software for pronunciation practice after the workshop than STRAT (p=.041)
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