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MILLA – Multimodal Interactive Language Learning Agent

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MILLA – Multimodal Interactive Language Learning Agent MILLA – Multimodal Interactive Language Learning Agent João Paulo Cabral1, Nick Campbell1, Shree Ganesh2, Emer Gilmartin1, Fasih Haider1, Eamonn Kenny1, Mina Kheirkhah3, Andrew Murphy1, Neasa Ní Chiaráin1, Thomas Pellegrini4, Odei Rey Orozko5 Trinity College Dublin, Ireland1; GCDH-University of Goettingen, Germany2; Institute for Advanced Studies in Basic Sciences, Zanjan, Iran3; Université de Toulouse ; IRIT, France4; Universidad del País Vasco, Bilbao, Spain5 1 Background 2 MILLA System Components Learning a new language involves the acquisition Tuition Manager: MILLA’s spoken dialogue and integration of a range of skills. A human tu- Tuition Manager (Figure 1) consults a two-level tor aids learners by (i) providing tasks suitable curriculum of language learning tasks, a learner to the learner’s needs, (ii) monitoring progress record and learner state module to greet and en- and adapting task content and delivery style, and roll learners, offer language learning submod- (iii) providing a source of speaking practice and ules, provide feedback, and monitor user state motivation. With the advent of audiovisual tech- with Kinect sensors. All of the tuition manager’s nology and the communicative paradigm in lan- interaction with the user can be performed using guage pedagogy, focus has shifted from written speech through a Cereproc Text-to-Speech (TTS) grammar and translation to developing commu- voice and Cereproc’s Python SDK (Cereproc, nicative competence in listening and spoken pro- 2014), and understanding via CMU’s Sphinx4 duction. The Common European Framework of ASR (Walker et al., 2004) through custom Py- Reference for Language Learning and Teaching thon bindings using W3C compliant Java Speech (CEFR) recently added a more integrative fifth Format Grammars. skill – spoken interaction - to the traditional four Tasks include spoken dialogue practice with two skills – reading and listening, and writing and chatbots, first language (L1) focused and general speaking (Little, 2006) . While second languages pronunciation, and grammar and vocabulary ex- have always been learned conversationally with ercises. Several speech recognition (ASR) en- negotiation of meaning between speakers of dif- gines (HTK, Google Speech) and text-to speech ferent languages sharing living or working envi- (TTS) voices (Mac and Windows system voices, ronments, these methods did not figure in formal Google Speech) are used in the modules to meet (funded) settings. However, with increased mo- the demands of particular tasks and to provide a bility and globalisation, many learners now need cast of voice characters which provide a variety language as a practical tool rather than simply as of speech models to the learner. Microsoft’s Ki- an academic achievement (Gilmartin, 2008). De- nect SDK (‘Kinect for Windows SDK’, 2014) is velopments in Computer Assisted Language used for gesture recognition and as a platform for Learning (CALL) have resulted in free and affect recognition. The tuition manager and all commercial language learning material for au- interfaces are written in Python 2.7, with addi- tonomous study. Much of this material transfers tional C#, Javascript, Java, and Bash coding in well-established text and audiovisual exercises to the Kinect, chat, Sphinx4, and pronunciation el- the computer screen. These resources greatly ements. For rapid prototyping the dialogue mod- help develop discrete skills, but the challenge of ules were first written in VoiceXML, then ported providing tuition and practice in the ‘fifth skill’, to Python modules. spoken interaction, remains. MILLA, developed Pronunciation Tuition: MILLA incorporates at the 2014 eNTERFACE workshop in Bilbao is two pronunciation modules, based on compari- a multimodal spoken dialogue system combining son of learner production with model production custom modules with existing web resources in a using the Goodness of Pronunciation (GOP) al- balanced curriculum, and, by integrating spoken gorithm (Witt & Young, 2000). GOP scoring dialogue, modelling some of the advantages of a involves two phases: 1) a free phone loop recog- human tutor. nition phase which determines the most likely phone sequence given the input speech without giving the ASR any information about the target sentence, and 2) a forced alignment phase which provides the ASR with the orthographic tran- scription and force aligns the speech signal with the expected phone sequence. Comparison of the log-likelihoods of the forced alignment and free recognition phases produces a GOP score. The first module is a focused pronunciation tu- tor using HTK ASR with the five-state 32 Gauss- ian mixture monophone acoustic models provid- ed with the Penn Aligner toolkit (Young, n.d.; Figure 1 MILLA Overview Yuan & Liberman, 2008) on the system’s local machine. In this module, phone specific thresh- tion with language teachers and learners, the sys- old scores were set by artificially inserting errors tem allows users to speak to the bot, or type chat in the pronunciation lexicon and running the al- responses. A chat log was also implemented in gorithm on native recordings, as in (Kanters, the interface, allowing the user to read back or Cucchiarini, & Strik, 2009). After preliminary replay previous interactions. tests, we constrained the free phone loop recog- Grammar, Vocabulary and External Re- niser for more robust behavior, using phone con- sources: MILLA’s curriculum includes a num- fusions common in specific L1’s to define con- ber of graded activities from the OUP’s English strained phone grammars. A database of com- File and the British Council’s Learn English mon errors in several L1s with test utterances websites. Wherever possible the system scrapes was built into the curriculum. any scores returned for exercises and incorpo- The second module, MySpeech, is a phrase rates them into the learner’s record, while in oth- level trainer hosted on University College Dub- er cases the progression and scoring system in- lin’s cluster and accessed by the system via In- cludes a time required to be spent on the exercis- ternet (Cabral et al., 2012). It tests pronunciation es before the user progresses to the next exercis- at several difficulty levels as described in (Kane es. There are also custom morphology and syntax & Carson-Berndsen, 2011). Difficulty levels are exercises created using Voxeo Prophecy to be introduced by incorporating Broad Phonetic ported to MILLA. Groups (BPGs) to cluster similar phones. A BFG User State and Gesture Recognition: MIL- consists of phones that share similar articulatory LA includes a learner state module to eventually feature information, for example plosives and infer learner boredom or involvement. As a first fricatives. There are three difficulty levels in the pass, gestures indicating various commands were MySpeech system: easy, medium and hard – the designed and incorporated into the system using easiest level includes a greater number of BPGs Microsoft’s Kinect SDK. The current implemen- in comparison to the harder levels. The tation comprises four gestures (Stop, I don’t MySpeech web interface consists of several know, Swipe Left/Right), which were designed numbered panels for the users to select sentences by tracking the skeletal movements involved and and practice their pronunciation by listening to extracting joint coordinates on the x, y, and z the selected sentence spoken by a native speaker planes to train the recognition process. Python’s and record their own version of the same sen- socket programming modules were used to tence. Finally, the results panel shows the detect- communicate between the Windows machine ed mispronunciation errors of a submitted utter- running the Kinect and the Mac laptop hosting ance using darker colours. MILLA. Spoken Interaction Tuition (Chat): To pro- vide spoken interaction practice, MILLA sends 3 Future work the user to Michael (Level1) or Susan (Level 2), MILLA is an ongoing project. In particular, work two chatbots created using the Pandorabots web- is in progress to add a Graphical User Interface based chatbot hosting service (Wallace, 2003). and avatar to provide a more immersive version The bots were first implemented in text-to-text and several new modules are planned. User trials form in AIML (Artificial Intelligence Markup are planned for the academic year 2014-15 in Language) and then TTS and ASR were added several centres providing language training to through the Web Speech API, conforming to immigrants in Ireland. W3C standards (W3C, 2014). Based on consulta- Acknowledgments W3C. (2014). Web Speech API Specification. Re- trieved 7 July 2014, from João Paulo Cabral and Eamonn Kenny are supported by the Science Foundation Ireland https://dvcs.w3.org/hg/speech-api/raw- (Grant 12/CE/I2267) as part of CNGL file/tip/speechapi.html (www.cngl.ie) at Trinity College Dublin.Sree Walker, W., Lamere, P., Kwok, P., Raj, B., Singh, R., Ganesh is supported by GCDH-University of Goettingen. Emer Gilmartin is supported by the Gouvea, E., … Woelfel, J. (2004). Sphinx-4: Science Foundation Ireland Fastnet Project, grant A flexible open source framework for speech 09/IN.1/I2631. Neasa Ní Chiaráin and Andrew recognition. Murphy are supported by the ABAIR Project, funded by the Irish Government’s Department of Wallace, R. S. (2003). Be Your Own Botmaster: The Arts, Heritage, and the Gaeltacht. Step By Step Guide to Creating, Hosting and Selling Your Own AI Chat Bot On Pan- References dorabots. ALICE AI foundations, Incorpo- Cabral, J. P., Kane, M., Ahmed, Z., Abou-Zleikha, rated. M., Székely, E., Zahra, A., … Schlögl, S. Witt, S. M., & Young, S. J. (2000). Phone-level pro- (2012). Rapidly Testing the Interaction Mod- nunciation scoring and assessment for inter- el of a Pronunciation Training System via active language learning. Speech Communi- Wizard-of-Oz. In LREC (pp. 4136–4142). cation, 30(2), 95–108. CereVoice Engine Text-to-Speech SDK | CereProc Young, S. (n.d.). HTK Speech Recognition Toolkit. Text-to-Speech. (2014). Retrieved 7 July Retrieved 7 July 2014, from 2014, from http://htk.eng.cam.ac.uk/ https://www.cereproc.com/en/products/sdk Yuan, J., & Liberman, M.
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