Using apps for pronunciation training: An empirical evaluation of the English File Pronunciation app

This study explores the potential of the English File Pronunciation (EFP) app to help foreign language learners improve their pronunciation. Participants were 52 Spanish EFL learners enrolled in an English Studies degree. Pre- and post-tests were used to assess the participants’ perception and production (imitative, controlled, and spontaneous) before and after training. The targets addressed were a range of segmental features that tend to be fossilised in the interlanguage of advanced Spanish EFL learners, namely English /æ ɑː ʌ ə/ and the /s – z/ contrast. Training took place over a period of two weeks in which participants used the English File pronunciation app for around 20 minutes a day. Participants were randomly assigned to two groups (control and experimental). However, after the post-test, the group that had acted as control started to receive instruction and, after two weeks, took a second post-test, therefore acting as experimental too. Training fostered substantial improvements in the learners’ perception and production of the target features, although the differences between groups were not statistically significant for every sound or in every task

The influence of affordances on user preferences for multimedia language learning applications.

This study investigates the influence of sensory and cognitive affordances on the user experience of mobile devices for multimedia language learning applications. A primarily audio-based language learning application – ‘Vowel Trainer’, was chosen against a comparison, text and picture-based language learning application – ‘Learn English for Taxi Drivers’. Impressions of the two applications were assessed on two different devices that have virtually the same interface and identical sound output (when headphones are used), but differ in physical size: the iPhone and the iPad. A mixed design was chosen, with native language as a group factor and device type (iPad vs. iPhone) and language application type (audio vs. video) as within groups factors. Assessments of sensory and cognitive affordances were made, along with measurement of learner preferences of each application. Data from 41 participants (21 native English speakers, 20 non-native English speakers) were analysed, revealing device differences in both audio and visual subjective quality ratings, despite only visual quality being affected by the device's physical limitations. We suggest that sensory affordances (indexed by subjective quality) are not simply a function of physical limitations, but are heavily influenced by context. The implications for developing design guidelines for language learning and other multimedia applications are discussed

Service Oriented Mobile Computing

La diffusione di concetti quali Pervasive e Mobile Computing introduce nell'ambito dei sistemi distribuiti due aspetti fondamentali: la mobilità dell'utente e l'interazione con l'ambiente circostante, favorite anche dal crescente utilizzo di dispositivi mobili dotati di connettività wireless come prodotti di consumo. Per estendere le funzionalità introdotte nell'ambito dei sistemi distribuiti dalle Architetture Orientate ai Servizi (SOA) e dal paradigma peer-to-peer anche a dispositivi dalle risorse limitate (in termini di capacità computazionale, memoria e batteria), è necessario disporre di un middleware leggero e progettato tenendo in considerazione tali caratteristiche. In questa tesi viene presentato NAM (Networked Autonomic Machine), un formalismo che descrive in modo esaustivo un sistema di questo tipo; si tratta di un modello teorico per la definizione di entità hardware e software in grado di condividere le proprie risorse in modo completamente altruistico. In particolare, il lavoro si concentra sulla definizione e gestione di un determinato tipo di risorse, i servizi, che possono essere offerti ed utilizzati da dispositivi mobili, mediante meccanismi di composizione e migrazione. NSAM (Networked Service-oriented Autonomic Machine) è una specializzazione di NAM per la condivisione di servizi in una rete peer-to-peer, ed è basato su tre concetti fondamentali: schemi di overlay, composizione dinamica di servizi e auto-configurazione dei peer. Nella tesi vengono presentate anche diverse attività applicative, che fanno riferimento all'utilizzo di due middleware sviluppati dal gruppo di Sistemi Distribuiti (DSG) dell'Università di Parma: SP2A (Service Oriented Peer-to-peer Architecture), framework per lo sviluppo di applicazioni distribuite attraverso la condivisione di risorse in una rete peer-to-peer, e Jxta-Soap che consente la condivisione di Web Services in una rete peer-to-peer JXTA. Le applicazioni realizzate spaziano dall'ambito della logistica, alla creazione di comunità per l'e-learning, all'Ambient Intelligence alla gestione delle emergenze, ed hanno come denominatore comune la creazione di reti eterogenee e la condivisione di risorse anche tra dispositivi mobili. Viene inoltre messo in evidenza come tali applicazioni possano essere ottimizzate mediante l'introduzione del framework NAM descritto, per consentire la condivisione di diversi tipi di risorse in modo efficiente e proattivo

Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities

Research and development work relating to assistive technology 2010-11 (Department of Health) Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197

The English IPA ear trainer

The affordances of the internet, such as social networking sites, wikis, and various communication tools have become integral to daily life. Educators are increasingly dedicating more time to developing applications to take advantage of the Web 2.0 age for second language learning purposes. However, many applications lack a solid pedagogical foundation or evidence-based reasoning in the application’s design. This study focused on the creation of a browser-based language learning application to aid language learners’ perception of a selection of English sounds. Using the training method known as High Variability Phonetic (HVPT), this thesis will present the creation of a learning application: the English IPA Ear Trainer. This paper will also detail the creation method and timeline in order to provide instructors with a model process to aid the creation of their own language learning application

The effect of HVP training in vowel perception on bilingual speech production

This is an accepted manuscript of an article published by Conscientia Beam in Research in English Language Teaching, available online at: http://www.conscientiabeam.com/archive/179/06-2021/1 The accepted version of the publication may differ from the final published version.Prior investigations (Giannakopoulou et al., 2013) have indicated high variability phonetic training intervention can help L2 English adult learners change the perception of vowels such that they shift their attention to primary cues (spectral features) rather than secondary cues (e.g. duration) to correctly identify vowels in L2. This experiment explores if high-variability training impacts on L2 adult learners’ production of L2 speech. Production samples from a prior experiment were used to conduct ratings of accuracy (Giannakopoulou, 2012). In the current experiment, the production samples were transcribed and rated for accuracy by twenty native English listeners. The intelligibility levels of L2 learners’ speech samples as indexed by higher accuracy in transcription were observed as having been rated higher following training than prior to training. The implications of the results are considered with regard to theories on the connection between speech production and perception, and Flege’s (1995) Speech Learning Model

Experts on e-learning: insights gained from listening to the student voice!

The Student Experience of e-Learning Laboratory (SEEL) project at the University of Greenwich was designed to explore and then implement a number of approaches to investigate learners’ experiences of using technology to support their learning. In this paper members of the SEEL team present initial findings from a University-wide survey of nearly a 1000 students. A selection of 90 ‘cameos’, drawn from the survey data, offer further insights into personal perceptions of e-learning and illustrate the diversity of students experiences. The cameos provide a more coherent picture of individual student experience based on the totality of each person’s responses to the questionnaire. Finally, extracts from follow-up case studies, based on interviews with a small number of students, allow us to ‘hear’ the student voice more clearly. Issues arising from an analysis of the data include student preferences for communication and social networking tools, views on the ‘smartness’ of their tutors’ uses of technology and perceptions of the value of e-learning. A primary finding and the focus of this paper, is that students effectively arrive at their own individualised selection, configuration and use of technologies and software that meets their perceived needs. This ‘personalisation’ does not imply that such configurations are the most efficient, nor does it automatically suggest that effective learning is occurring. SEEL reminds us that learners are individuals, who approach learning both with and without technology in their own distinctive ways. Hearing, understanding and responding to the student voice is fundamental in maximising learning effectiveness. Institutions should consider actively developing the capacity of academic staff to advise students on the usefulness of particular online tools and resources in support of learning and consider the potential benefits of building on what students already use in their everyday lives. Given the widespread perception that students tend to be ‘digital natives’ and academic staff ‘digital immigrants’ (Prensky, 2001), this could represent a considerable cultural challenge

英語を学ぶ日本人学生は音素 /l/ の発音を簡単に取得できるか？

One of the most vexing aspects of learning English conversation for students in Japan seems to be mastering the phoneme /l/, and this study sets out to describe the linguistic phenomenon in detail. Just over 100 first-year college students in four conversation classes were recorded speaking in pairs over a three-month period—in pre-, mid-, and post-course tests. The /l/ frequency errors and linguistic environments were tabulated. It was found that just 10.9% of all /l/s uttered in all environments were deemed unacceptable or non-nativelike. Students made significantly more errors on the pre-course test than on the mid- and post-course tests. But, when just two words which also appear in the Japanese language with katakana pronunciations—policy and complex —were eliminated from the pre-course test tabulations, the progress made by students during the course was not statistically significant. A final interesting finding was that /l/ was the hardest to pronounce when between vowels (eg., hello ) or in consonant clusters (eg., play). The classroom implications for teachers are described.日本人学生の英語会話学習において最も厄介な問題のひとつは音素 /l/ の発音習得であろう。本研究ではこの言語学的現象の詳細な記述を試みる。４つの会話クラスのあわせて100人あまりの大学１年生に対し，３ヶ月以上の期間にわたり，事前テスト，中間テスト，事後テストにおけるパートナー作業での発話を録音した。また，音素 /l/ の発音において頻繁に起こる誤りとその言語学的な出現環境の一覧を表の形でまとめた。 本研究により判明したのは，音素 /l/ の全出現環境における全発音の10.9%だけが容認できない，あるいは母語話者のようではないと判断されたことである。学生たちは，中間テストや事後テストに比較して事前テストにおいて明らかにより多くの誤りを犯している。しかし，日本語でもカタカナ表記で使われる「ポリシー」と「コンプレックス」という２語を事前テストの表から削除すると，統計的には授業期間中における学生の進歩は見られない。最後の興味深い発見は，音素 /l/ は母音の間（例：Hello）や子音連鎖の中に（例：play）現れる時，発音するのが最も難しいということである。授業を行う教員のための，本研究から得られた教育上の示唆についても記述した

Enabling Deep Intelligence on Embedded Systems

As deep learning for resource-constrained systems become more popular, we see an increased number of intelligent embedded systems such as IoT devices, robots, autonomous vehicles, and the plethora of portable, wearable, and mobile devices that are feature-packed with a wide variety of machine learning tasks. However, the performance of DNNs (deep neural networks) running on an embedded system is significantly limited by the platform's CPU, memory, and battery-size; and their scope is limited to simplistic inference tasks only. This dissertation proposes on-device deep learning algorithms and supporting hardware designs, enabling embedded systems to efficiently perform deep intelligent tasks (i.e., deep neural networks) that are high-memory-footprint, compute-intensive, and energy-hungry beyond their limited computing resources. We name such on-device deep intelligence on embedded systems as Embedded Deep Intelligence. Specifically, we introduce resource-aware learning strategies devised to overcome the four fundamental constraints of embedded systems imposed on the way towards Embedded Deep Intelligence, i.e., in-memory multitask learning via introducing the concept of Neural Weight Virtualization, adaptive real-time learning via introducing the concept of SubFlow, opportunistic accelerated learning via introducing the concept of Neuro.ZERO, and energy-aware intermittent learning, which tackles the problems of the small size of memory, dynamic timing constraint, low-computing capability, and limited energy, respectively. Once deployed in the field with the proposed resource-aware learning strategies, embedded systems are not only able to perform deep inference tasks on sensor data but also update and re-train their learning models at run-time without requiring any help from any external system. Such an on-device learning capability of Embedded Deep Intelligence makes an embedded intelligent system real-time, privacy-aware, secure, autonomous, untethered, responsive, and adaptive without concern for its limited resources.Doctor of Philosoph