We argue that the field of spoken CALL needs a shared task in order to facilitate comparisons between different groups and methodologies, and describe a concrete example of such a task, based on data collected from a speech-enabled online tool which has been used to help young Swiss German teens practise skills in English conversation. Items are prompt-response pairs, where the prompt is a piece of German text and the response is a recorded English audio file. The task is to label pairs as “accept” or “reject”, accepting responses which are grammatically and linguistically correct to match a set of hidden gold standard answers as closely as possible. Initial resources are provided so that a scratch system can be constructed with a minimal investment of effort, and in particular without necessarily using a speech recogniser. Training data for the task will be released in June 2016, and test data in January 2017.

We present an English-L2 child learner speech corpus, produced by 14 year old Swiss German-L1 students in their third year of learning English, which is currently in the process of being collected. The collection method uses a web-enabled multimodal language game implemented using the CALL-SLT platform, in which subjects hold prompted conversations with an animated agent. Prompts consist of a short animated Engligh-language video clip together with a German-language piece of text indicating the semantic content of the requested response. Grammar-based speech understanding is used to decide whether responses are accepted or rejected, and dialogue flow is controlled using a simple XML-based scripting language; the scripts are written to allow multiple dialogue paths, the choice being made randomly. The system is gamified using a score-and-badge framework with four levels of badges. We describe the application, the data collection and annotation procedures, and the initial tranche of data. The full corpus, when complete, should contain at least 5,000 annotated utterances.

We describe CALL-SLT, a speech-enabled Computer-Assisted Language Learning application where the central idea is to prompt the student with an abstract representation of what they are supposed to say, and then use a combination of grammar-based speech recognition and rule-based translation to rate their response. The system has been developed to the level of a mature prototype, freely deployed on the web, with versions for several languages. We present an overview of the core system architecture and the various types of content we have developed. Finally, we describe several evaluations, the last of which is a study carried out over about a week using 130 subjects recruited through the Amazon Mechanical Turk, in which CALL-SLT was contrasted against a control version where the speech recognition component was disabled. The improvement in student learning performance between the two groups was significant at p < 0.02.

We describe a multilingual Open Source CALL game, CALL-SLT, which reuses speech translation technology developed using the Regulus platform to create an automatic conversation partner that allows intermediate-level language students to improve their fluency. We contrast CALL-SLT with Wang's and Seneff's ``translation game'' system, in particular focussing on three issues. First, we argue that the grammar-based recognition architecture offered by Regulus is more suitable for this type of application; second, that it is preferable to prompt the student in a language-neutral form, rather than in the L1; and third, that we can profitably record successful interactions by native speakers and store them to be reused as online help for students. The current system, which will be demoed at the conference, supports four L2s (English, French, Japanese and Swedish) and two L1s (English and French). We conclude by describing an evaluation exercise, where a version of CALL-SLT configured for English L2 and French L1 was used by several hundred high school students. About half of the subjects reported positive impressions of the system.