Re-examining Phonological and Lexical Correlates of Second Language Comprehensibility:The Role of Rater Experience

Few researchers and teachers would disagree that some linguistic aspects of second language (L2) speech are more crucial than others for successful communication. Underlying this idea is the assumption that communicative success can be broadly defined in terms of speakers’ ability to convey the intended meaning to the interlocutor, which is frequently captured through a listener-based rating of comprehensibility or ease of understanding (e.g. Derwing & Munro, 2009; Levis, 2005). Previous research has shown that communicative success – for example, as defined through comprehensible L2 speech – depends on several linguistic dimensions of L2 output, including its segmental and suprasegmental pronunciation, fluency-based characteristics, lexical and grammatical content, as well as discourse structure (e.g. Field, 2005; Hahn, 2004; Kang et al., 2010; Trofimovich & Isaacs, 2012). Our chief objective in the current study was to explore the L2 comprehensibility construct from a language assessment perspective (e.g. Isaacs & Thomson, 2013), by targeting rater experience as a possible source of variance influencing the degree to which raters use various characteristics of speech in judging L2 comprehensibility. In keeping with this objective, we asked the following question: What is the extent to which linguistic aspects of L2 speech contributing to comprehensibility ratings depend on raters’ experience

Second Language Pronunciation Assessment:A Look at the Present and the Future

This book is open access under a CC BY licence. It spans the areas of assessment, second language acquisition (SLA) and pronunciation and examines topical issues and challenges that relate to formal and informal assessments of second language (L2) speech in classroom, research and real-world contexts. It showcases insights from assessing other skills (e.g. listening and writing) and highlights perspectives from research in speech sciences, SLA, psycholinguistics and sociolinguistics, including lingua franca communication, with concrete implications for pronunciation assessment. This collection will help to establish commonalities across research areas and facilitate greater consensus about key issues, terminology and best practice in L2 pronunciation research and assessment. Due to its interdisciplinary nature, this book will appeal to a mixed audience of researchers, graduate students, teacher-educators and exam board staff with varying levels of expertise in pronunciation and assessment and wide-ranging interests in applied linguistics.EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

' "The Tale of the Tribe": The Twentieth-Century Alliterative Revival.'

This thesis studies the revival of Old English- and Norse-inspired alliterative versification in twentieth-century English poetry and poetics. It is organised as a chronological sequence of three case-studies: three authors, heirs to Romantic Nationalism, writing at twentieth-century intersections between Modernism, Postmodernism, and Medievalism. It indicates why this form attracted revival; which medieval models were emulated, with what success, in which modern works: the technique and mystique of alliterative verse as a modern mode. It differs from previous scholarship by advocating Kipling and Tolkien, by foregrounding the primacy of language, historical linguistics, especially the philological reconstruction of Germanic metre; and by, accordingly, methodological emphasis on formal scansion, taking account of audio recordings of Pound and Tolkien performing their poetry. It proposes the revived form as archaising, epic, mythopoeic, constructed by its exponents as an authentic poetic speech symbolising an archetypical Englishness—‘The Tale of the Tribe’. A trope emerges of revival of the culturally-‘buried’ native and innate, an ancestral lexico-metrical heritage conjured back to life. A substantial Introduction offers a primer of Old English metre and style: how it works, and what it means, according to Eduard Sievers’ (1850-1932) reconstruction. Chapter I promotes Rudyard Kipling (1865-1936) as pioneering alliterative poet, his engagement with Old-Northernism, runes, and retelling of the myth of Weland. Chapter II assesses the impact of Anglo-Saxon on and through Ezra Pound (1885-1972). Scansions of his ‘Seafarer’ and Cantos testify to the influence of Saxonising versification in the development of Pound’s Modernist language and free verse. Chapter III exhibits the alliterative oeuvre of J. R. R. Tolkien (1892-1973), featuring close readings of verse from Lord of the Rings. The Conclusion contends that twentieth-century English poetry should be recognised as evincing an ambitious alliterative revival, impossible before, and that this ancient metre is likely to endure into the future

Exploring Cross-linguistic Effects and Phonetic Interactions in the Context of Bilingualism

This Special Issue includes fifteen original state-of-the-art research articles from leading scholars that examine cross-linguistic influence in bilingual speech. These experimental studies contribute to the growing number of studies on multilingual phonetics and phonology by introducing novel empirical data collection techniques, sophisticated methodologies, and acoustic analyses, while also presenting findings that provide robust theoretical implications to a variety of subfields, such as L2 acquisition, L3 acquisition, laboratory phonology, acoustic phonetics, psycholinguistics, sociophonetics, blingualism, and language contact. These studies in this book further elucidate the nature of phonetic interactions in the context of bilingualism and multilingualism and outline future directions in multilingual phonetics and phonology research

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

Bag-of-words representations for computer audition

Computer audition is omnipresent in everyday life, in applications ranging from personalised virtual agents to health care. From a technical point of view, the goal is to robustly classify the content of an audio signal in terms of a defined set of labels, such as, e.g., the acoustic scene, a medical diagnosis, or, in the case of speech, what is said or how it is said. Typical approaches employ machine learning (ML), which means that task-specific models are trained by means of examples. Despite recent successes in neural network-based end-to-end learning, taking the raw audio signal as input, models relying on hand-crafted acoustic features are still superior in some domains, especially for tasks where data is scarce. One major issue is nevertheless that a sequence of acoustic low-level descriptors (LLDs) cannot be fed directly into many ML algorithms as they require a static and fixed-length input. Moreover, also for dynamic classifiers, compressing the information of the LLDs over a temporal block by summarising them can be beneficial. However, the type of instance-level representation has a fundamental impact on the performance of the model. In this thesis, the so-called bag-of-audio-words (BoAW) representation is investigated as an alternative to the standard approach of statistical functionals. BoAW is an unsupervised method of representation learning, inspired from the bag-of-words method in natural language processing, forming a histogram of the terms present in a document. The toolkit openXBOW is introduced, enabling systematic learning and optimisation of these feature representations, unified across arbitrary modalities of numeric or symbolic descriptors. A number of experiments on BoAW are presented and discussed, focussing on a large number of potential applications and corresponding databases, ranging from emotion recognition in speech to medical diagnosis. The evaluations include a comparison of different acoustic LLD sets and configurations of the BoAW generation process. The key findings are that BoAW features are a meaningful alternative to statistical functionals, offering certain benefits, while being able to preserve the advantages of functionals, such as data-independence. Furthermore, it is shown that both representations are complementary and their fusion improves the performance of a machine listening system.Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw. die akustische Umgebung der Aufnahme, eine medizinische Diagnose oder - im Falle von Sprache - was gesagt wird oder wie es gesagt wird. Übliche Ansätze hierzu verwenden maschinelles Lernen, d.h., es werden anwendungsspezifische Modelle anhand von Beispieldaten trainiert. Trotz jüngster Erfolge beim Ende-zu-Ende-Lernen mittels neuronaler Netze, in welchen das unverarbeitete Audiosignal als Eingabe benutzt wird, sind Modelle, die auf definierten akustischen Merkmalen basieren, in manchen Bereichen weiterhin überlegen. Dies gilt im Besonderen für Einsatzzwecke, für die nur wenige Daten vorhanden sind. Allerdings besteht dabei das Problem, dass Zeitfolgen von akustischen Deskriptoren in viele Algorithmen des maschinellen Lernens nicht direkt eingespeist werden können, da diese eine statische Eingabe fester Länge benötigen. Außerdem kann es auch für dynamische (zeitabhängige) Klassifikatoren vorteilhaft sein, die Deskriptoren über ein gewisses Zeitintervall zusammenzufassen. Jedoch hat die Art der Merkmalsdarstellung einen grundlegenden Einfluss auf die Leistungsfähigkeit des Modells. In der vorliegenden Dissertation wird der sogenannte Bag-of-Audio-Words-Ansatz (BoAW) als Alternative zum Standardansatz der statistischen Funktionale untersucht. BoAW ist eine Methode des unüberwachten Lernens von Merkmalsdarstellungen, die von der Bag-of-Words-Methode in der Computerlinguistik inspiriert wurde, bei der ein Textdokument als Histogramm der vorkommenden Wörter beschrieben wird. Das Toolkit openXBOW wird vorgestellt, welches systematisches Training und Optimierung dieser Merkmalsdarstellungen - vereinheitlicht für beliebige Modalitäten mit numerischen oder symbolischen Deskriptoren - erlaubt. Es werden einige Experimente zum BoAW-Ansatz durchgeführt und diskutiert, die sich auf eine große Zahl möglicher Anwendungen und entsprechende Datensätze beziehen, von der Emotionserkennung in gesprochener Sprache bis zur medizinischen Diagnostik. Die Auswertungen beinhalten einen Vergleich verschiedener akustischer Deskriptoren und Konfigurationen der BoAW-Methode. Die wichtigsten Erkenntnisse sind, dass BoAW-Merkmalsvektoren eine geeignete Alternative zu statistischen Funktionalen darstellen, gewisse Vorzüge bieten und gleichzeitig wichtige Eigenschaften der Funktionale, wie bspw. die Datenunabhängigkeit, erhalten können. Zudem wird gezeigt, dass beide Darstellungen komplementär sind und eine Fusionierung die Leistungsfähigkeit eines Systems des maschinellen Hörens verbessert