Directions for the future of technology in pronunciation research and teaching

This paper reports on the role of technology in state-of-the-art pronunciation research and instruction, and makes concrete suggestions for future developments. The point of departure for this contribution is that the goal of second language (L2) pronunciation research and teaching should be enhanced comprehensibility and intelligibility as opposed to native-likeness. Three main areas are covered here. We begin with a presentation of advanced uses of pronunciation technology in research with a special focus on the expertise required to carry out even small-scale investigations. Next, we discuss the nature of data in pronunciation research, pointing to ways in which future work can build on advances in corpus research and crowdsourcing. Finally, we consider how these insights pave the way for researchers and developers working to create research-informed, computer-assisted pronunciation teaching resources. We conclude with predictions for future developments

A system for adaptive high-variability segmental perceptual training: Implementation, effectiveness, transfer

Many types of L2 phonological perception are often difficult to acquire without instruction. These difficulties with perception may also be related to intelligibility in production. Instruction on perception contrasts is more likely to be successful with the use of phonetically variable input made available through computer-assisted pronunciation training. However, few computer-assisted programs have demonstrated flexibility in diagnosing and treating individual learner problems or have made effective use of linguistic resources such as corpora for creating training materials. This study introduces a system for segmental perceptual training that uses a computational approach to perception utilizing corpus-based word frequency lists, high variability phonetic input, and text-to-speech technology to automatically create discrimination and identification perception exercises customized for individual learners. The effectiveness of the system is evaluated in an experiment with pre- and post-test design, involving 32 adult Russian-speaking learners of English as a foreign language. The participants’ perceptual gains were found to transfer to novel voices, but not to untrained words. Potential factors underlying the absence of word-level transfer are discussed. The results of the training model provide an example for replication in language teaching and research settings

Essential English for Pilotage and Tug Assistance - Proposal for SMCP Extension

This paper aims at presenting the activities undertaken since 2012 by the G.A.M.E. – Gesellschaft für Ausbildung in Maritimem Englisch (German Association for Maritime English) with the seat at Bremen University of Applied Sciences, Nautical Department and presided by Capt. Willi Wittig, Head of the Department. The Association gathers Maritime English instructors and maritime professionals who have recently focused on updating the existing Standard Marine Communication Phrases – SMCP – in order to better match the ever growing requirements in maritime aff airs. The emphasis has been put on the pilotage and tug assistance phrases, as the existing body of phrases has not been felt entirely suitable to the activities performed. Thus, Capt. Matthias Meyer, master mariner and lecturer at the Nautical Department of the University of Applied Sciences in Bremen, former elder brother of Port Pilot Society Bremerhaven, was entrusted with the task of proposing a further development of the phrases related to this particular seafaring activity. The other lecturers, including the author of the paper, contributed during the 2014 G.A.M.E. summer seminar to Capt. Meyer’s proposal purely from the linguistic or methodical point of view

Automatic classification of lexical stress errors for German CAPT

Abstract Lexical stress plays an important role in the prosody of German, and presents a considerable challenge to native speakers of languages such as French who are learning German as a foreign language. These learners stand to benefit greatly from Computer-Assisted Pronunciation Training (CAPT) systems which can offer individualized corrective feedback on such errors, and reliable automatic detection of these errors is a prerequisite for developing such systems. With this motivation, this paper presents an exploration of the use of machine learning methods to classify non-native German lexical stress errors. In classification experiments using a manually-annotated corpus of German word utterances by native French speakers, the highest observed agreement between the classifier's output and the gold-standard labels exceeded the inter-annotator agreement between humans asked to classify lexical stress errors in the same data. These results establish the viability of classification-based diagnosis of lexical stress errors for German CAPT

Pronunciation development and instruction in distance language learning

The goal of this study was to explore how distance language learners’ pronunciation skills develop with and without targeted pronunciation training during their first semester of university language instruction. To this end, a new computer-assisted method of pronunciation instruction was designed, and its effectiveness, as well as learners’ experiences with the method, were assessed. The study was conducted over the course of one semester with 67 distance learners of German. Perception and oral production skills on the word and sentence level were assessed at the beginning and end of the semester for a treatment group that received targeted pronunciation instruction and a control group that did not receive pronunciation instruction, but otherwise followed the same curriculum. The results of the study showed that distance learners’ pronunciation skills did not improve significantly over the course of the semester in the absence of targeted pronunciation training. Results further indicated that learners who received targeted pronunciation training improved significantly from pre- to posttest and significantly outperformed learners in the control group on measures of perception and production accuracy at the end of the semester. These findings suggest that distance language instruction can benefit from including targeted pronunciation training

Persian ITAs and Speech Comprehensibility: Using CAPT for Pronunciation Improvement

Abstract It has been shown in the past that International Teaching Assistants (ITAs) struggle with phonological and communication issues in the classroom (Pickering, 1999; 2001). This issue leads to misunderstandings between ITAs and undergraduate students, frustrating them both as well as the parents of the students and the departments. However, studies have shown that with the right training, ITAs can focus on suprasegmental features, improving their speech comprehensibility and intelligibility (Gorusch, 2011). This study investigates the effect of Computer Assisted Pronunciation Teaching (CAPT) via tutorial videos and visual feedback on the improvement of ITAs’ speech comprehensibility. Across 5 US universities, 60 Persian ITAs, a video group (n=20), a visual feedback group(n=21), and a control group (n=19), completed an oral production pretest and recorded five diagnostic sentences plus spontaneous speech files. Over the next six weeks, all groups received in-person non-CAPT instruction, but the video group received and watched extra eight tutorial videos designed to target suprasegmental features and the feedback group was exposed to Praat visual feedback. Participants were also paired with a pronunciation tutor who provided instruction and feedback once a week. A perception posttest was administered, and the same 5 sentences with the spontaneous talk were once again recorded. The pre-and post-treatment sentences were then rated by 169 undergraduate students for comprehensibility. The findings of this study provide a greater understanding of how explicit instruction of pronunciation through CAPT can improve the speech comprehensibility of ITAs. The number of international people in academic and professional contexts is rising, it is necessary to guide them through appropriate instruction to improve their communication quality. The results of this study suggest that even short intervention programs that include targeted in-person tutoring, tutorial videos, and visual feedback may improve ITAs’ communications. Results also imply the need for pronunciation support for ITAs in their respective academic institutions

ESTABLISHING A METHODOLOGY FOR BENCHMARKING SPEECH SYNTHESIS FOR COMPUTER-ASSISTED LANGUAGE LEARNING (CALL)

Despite the new possibilities that speech synthesis brings about, few Computer-Assisted Language Learning (CALL) applications integrating speech synthesis have found their way onto the market. One potential reason is that the suitability and benefits of the use of speech synthesis in CALL have not been proven. One way to do this is through evaluation. Yet, very few formal evaluations of speech synthesis for CALL purposes have been conducted. One possible reason for the neglect of evaluation in this context is the fact that it is expensive in terms of time and resources. An important concern given that there are several levels of evaluation from which such applications would benefit. Benchmarking, the comparison of the score obtained by a system with that obtained by one which is known, to guarantee user satisfaction in a standard task or set of tasks, is introduced as a potential solution to this problem. In this article, we report on our progress towards the development of one of these benchmarks, namely a benchmark for determining the adequacy of speech synthesis systems for use in CALL. We do so by presenting the results of a case study which aimed to identify the criteria which determine the adequacy of the output of speech synthesis systems for use in its various roles in CALL with a view to the selection of benchmark tests which will address these criteria. These roles (reading machine, pronunciation model, and conversational partner) are also discussed here. An agenda for further research and evaluation is proposed in the conclusion

Enhancing Listening and Spoken Skills in Spanish Connected Speech for Anglophones

Native speech is directed towards native listeners, not designed for comprehension and analysis by language learners. Speed of delivery, or economy of effort, produces a speech signal to which the native listener can assign the correct words. There are no discrete words in the speech signal itself therefore there is often a linguistic barrier in dealing with the local spoken language.The creation, development and application of the Dynamic Spanish Speech Corpus (DSSC) facilitated an empirically-based appreciation of speaking speed and prosody as obstacles to intelligibility for learners of Spanish. “Duologues”, natural, relaxed dialogues recorded in such a manner that each interlocutor’s performance can be studied in isolation, thus avoiding problems normally caused by cross-talk and back-channelling, made possible the identification of the key phonetic features of informal native-native dialogue, and ultimately, the creation of high quality assets/ research data based on natural (unscripted) dialogues recorded at industry audio standards.These assets were used in this study, which involved documenting productive and receptive intelligibility problems when L2 users are exposed to the Spanish speech of native speakers. The aim was to observe where intelligibility problems occur and to determine the reasons for this, based on effects of the first language of the subjects, and other criteria, such as number of years learning/using Spanish, previous exposure to spoken Spanish and gender. This was achieved by playing recorded extracts/ snippets from the DSSC to which a time-scaling tool was applied

Learning languages from parallel corpora

This work describes a blueprint for an application that generates language learning exercises from parallel corpora. Word alignment and parallel structures allow for the automatic assessment of sentence pairs in the source and target languages, while users of the application continuously improve the quality of the data with their interactions, thus crowdsourcing parallel language learning material. Through triangulation, their assessment can be transferred to language pairs other than the original ones if multiparallel corpora are used as a source. Several challenges need to be addressed for such an application to work, and we will discuss three of them here. First, the question of how adequate learning material can be identified in corpora has received some attention in the last decade, and we will detail what the structure of parallel corpora implies for that selection. Secondly, we will consider which type of exercises can be generated automatically from parallel corpora such that they foster learning and keep learners motivated. And thirdly, we will highlight the potential of employing users, that is both teachers and learners, as crowdsourcers to help improve the material