Formative Feedback in an Interactive Spoken CALL System
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Abstract:
By definition spoken dialogue CALL systems should be easy to use and understand. However, interaction in this context is often far from unhindered. In this paper we introduce a formative feedback mechanism in our CALL system, which can monitor interaction, report errors and provide advice and suggestions to users. The distinctive feature of this mechanism is the ability to combine information from different sources and decide on the most pertinent feedback, which can also be adapted in terms of phrasing, style and language. We conducted experiments at three secondary schools in German-speaking Switzerland and the obtained results suggest that our feedback mechanism helps students during interaction and contributes as a motivating factor.Keywords:
Spoken Language
Feature (linguistics)
Corrective Feedback
Factor (programming language)
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Spoken Language
Utterance
Interface (matter)
Natural language understanding
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In this paper, we present evidence that providing users of a speech to speech translation system for emergency diagnosis (MedSLT) with a tool that helps them to learn the coverage greatly improves their success in using the system. In MedSLT, the system uses a grammar-based recogniser that provides more predictable results to the translation component. The help module aims at addressing the lack of robustness inherent in this type of approach. It takes as input the result of a robust statistical recogniser that performs better for out-of-coverage data and produces a list of in-coverage example sentences. These examples are selected from a defined list using a heuristic that prioritises sentences maximising the number of N-grams shared with those extracted from the recognition result.
Speech translation
Robustness
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Training set
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We present an enhanced method for user feedback in an autonomous learning system that includes a spoken dialogue system to manage the interactions between the users and the system.By means of a rule-based natural language understanding module and a state-based dialogue manager we allow the users to update the preferences learnt by the system from the data obtained from different sensors.The design of the dialogue together with the storage of context information (the previous dialogue turns and the current state of the dialogue) ensures highly natural interactions, reducing the number of dialogue turns and making it possible to use complex linguistic constructions instead of isolated commands.
Natural language understanding
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Speech translation
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Summary form only given. The need for accurate and flexible evaluation frameworks for spoken and multimodal dialogue systems has become crucial. In the early design phases of spoken dialogue systems, it is worthwhile evaluating the user's easiness in interacting with different dialogue strategies, rather than the efficiency of the dialogue system in providing the required information. The success of a task-oriented dialogue system greatly depends on the ability of providing a meaningful match between user's expectations and system capabilities, and a good trade-off improves the user's effectiveness. The evaluation methodology requires three steps. The first step has the goal of individuating the different tokens and relations that constitute the user mental model of the task. Once tokens and relations are considered for designing one or more dialogue strategies, the evaluation enters its second step which is constituted by a between-group experiment. Each strategy is tried by a representative set of experimental subjects. The third step includes measuring user effectiveness in providing the spoken dialogue system with the information it needs to solve the task. The paper argues that the application of the three-steps evaluation method may increase our understanding of the user mental model of a task during early stages of development of a spoken language agent. Experimental data supporting this claim are reported.
Spoken Language
Mental model
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We describe a version of the CALL-SLT spoken conversation partner adapted for use on a mobile device. The system allows beginner/intermediate language students to practise interactive spoken language skills, using a version of MIT’s “translation game” concept: the machine prompts using an abstract description of what the student is supposed to say, and the student gives a spoken response. We focus on issues concerning the mobile version, which uses a Internet-based client/server configuration in which most processing is performed on the remote server; the client is controlled using accelerometer-based gesture recognition. We present an initial evaluation addressing two central questions: comparison of performance on mobile, Web and desktop versions and accuracy of gesture recognition
Spoken Language
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AUTOMATIC EVALUATION OF THE PRONUNCIATION WITH CALL-SLT, A CONVERSATION PARTNER EXCLUSIVELY BASED ON SPEECH RECOGNITION
Pronunciation
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Converse
Spoken Language
Natural language understanding
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