For many years, the vocal tract shape has been approximated by one-dimensional (1D) area functions to study the production of voice. More recently, 3D approaches allow one to deal with the complex 3D vocal tract, although area-based 3D geometries of circular cross-section are still in use. However, little is known about the influence of performing such a simplification, and some alternatives may exist between these two extreme options. To this aim, several vocal tract geometry simplifications for vowels [A], [i], and [u] are investigated in this work. Six cases are considered, consisting of realistic, elliptical, and circular cross-sections interpolated through a bent or straight midline. For frequencies below 4-5 kHz, the influence of bending and cross-sectional shape has been found weak, while above these values simplified bent vocal tracts with realistic cross-sections are necessary to correctly emulate higher-order mode propagation. To perform this study, the finite element method (FEM) has been used. FEM results have also been compared to a 3D multimodal method and to a classical 1D frequency domain model.

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The aim of this paper is to summarise how pronunciation feedback on the phoneme level should be given in computer-assisted pronunciation training (CAPT) in order to be effective. The study contains a literature survey of feedback in the language classroom, interviews with language teachers and their students about their attitudes towards pronunciation feedback, and observations of how feedback is given in their classrooms. The study was carried out using focus group meetings, individual semi-structured interviews and classroom observations. The feedback strategies that were advocated and observed in the study on pronunciation feedback from human teachers were implemented in a computer-animated language tutor giving articulation feedback. The virtual tutor was subsequently tested in a user trial and evaluated with a questionnaire. The article proposes several feedback strategies that would improve the pedagogical soundness of CAPT systems.

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Four different interaction styles for the social robot Furhat acting as a host in spoken conversation practice with two simultaneous language learners have been developed, based on interaction styles of human moderators of language cafés. We first investigated, through a survey and recorded sessions of three-party language café style conversations, how the interaction styles of human moderators are influenced by different factors (e.g., the participants language level and familiarity). Using this knowledge, four distinct interaction styles were developed for the robot: sequentially asking one participant questions at the time (Interviewer); the robot speaking about itself, robots and Sweden or asking quiz questions about Sweden (Narrator); attempting to make the participants talk with each other (Facilitator); and trying to establish a three-party robot-learnerlearner interaction with equal participation (Interlocutor). A user study with 32 participants, conversing in pairs with the robot, was carried out to investigate how the post-session ratings of the robot's behavior along different dimensions (e.g., the robot's conversational skills and friendliness, the value of practice) are influenced by the robot's interaction style and participant variables (e.g., level in the target language, gender, origin). The general findings were that Interviewer received the highest mean rating, but that different factors influenced the ratings substantially, indicating that the preference of individual participants needs to be anticipated in order to improve learner satisfaction with the practice. We conclude with a list of recommendations for robot-hosted conversation practice in a second language. Keywords Robot-assisted language learning • Multi-party human-robot interaction • Collaborative language learning • conversational practice 2 Collaborative Robot-Assisted Language Learning Developing a setup for a humanoid robot that can engage in a realistic social conversation with two L2 learners simultaneously is, to the authors' knowledge, unprecedented in Robot-Assisted Language Learning (RALL).

Three-dimensional (3-D) numerical approaches for voice production are currently being investigated and developed. Radiation losses produced when sound waves emanate from the mouth aperture are one of the key aspects to be modeled. When doing so, the lips are usually removed from the vocal tract geometry in order to impose a radiation impedance on a closed cross-section, which speeds up the numerical simulations compared to free-field radiation solutions. However, lips may play a significant role. In this work, the lips' effects on vowel sounds are investigated by using 3-D vocal tract geometries generated from magnetic resonance imaging. To this aim, two configurations for the vocal tract exit are considered: with lips and without lips. The acoustic behavior of each is analyzed and compared by means of time-domain finite element simulations that allow free-field wave propagation and experiments performed using 3-D-printed mechanical replicas. The results show that the lips should be included in order to correctly model vocal tract acoustics not only at high frequencies, as commonly accepted, but also in the low frequency range below 4 kHz, where plane wave propagation occurs.

This study has been performed in order to evaluate a prototype for the human-computer interface of a computer-based speech training aid named ARTUR. The main feature of the aid is that it can give suggestions on how to improve articulations. Two user groups were involved: three children aged 9-14 with extensive experience of speech training with therapists and computers, and three children aged 6, with little or no prior experience of computer-based speech training. All children had general language disorders. The study indicates that the present interface is usable without prior training or instructions, even for the younger children, but that more motivational factors should be introduced. The granularity of the mesh that classifies mispronunciations was satisfactory, but the flexibility and level of detail of the feedback should be developed further.

This article analyses how robot-learner interaction in robotassisted language learning (RALL) is influenced by the interaction behaviour of the robot. Since the robot behaviour is to a large extent determined by the combination of teaching strategy, robot role and robot type, previous studies in RALL are first summarised with respect to which combinations that have been chosen, the rationale behind the choice and the effects on interaction and learning. The goal of the summary is to determine a suitable pedagogical setup for RALL with adult learners, since previous RALL studies have almost exclusively been performed with children and youths. A user study in which 33 adult second language learners practice Swedish in three-party conversations with an anthropomorphic robot head is then presented. It is demonstrated how different robot interaction behaviours influence interaction between the robot and the learners and between the two learners. Through an analysis of learner interaction, collaboration and learner ratings for the different robot behaviours, it is observed that the learners were most positive towards the robot behaviour that focused on interviewing one learner at the time (highest average ratings), but that they were the most active in sessions when the robot encouraged learner-learner interaction. Moreover, the preferences and activity differed between learner pairs, depending on, e.g., their proficiency level and how well they knew the peer. It is therefore concluded that the robot behaviour needs to adapt to such factors. In addition, collaboration with the peer played an important part in conversation practice sessions to deal with linguistic difficulties or communication problems with the robot.