Modern technologies in teaching FLT — страница 7

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provided they are presented along with other types of feedback, as well as with instructions on how to interpret the displays. Teaching Linguistic Structures and Limited Conversation Apart from supporting systems for teaching basic pronunciation and literacy skills, ASR technology is being deployed in automated language tutors that offer practice in a variety of higher-level linguistic skills ranging from highly constrained grammar and vocabulary drills to limited conversational skills in simulated real-life situations. Prior to implementing any such system, a choice needs to be made between two fundamentally different system design types: closed response vs. open response design. In both designs, students are prompted for speech input by a combination of written, spoken, or

graphical stimuli. However, the designs differ significantly with reference to the type of verbal computer-student interaction they support. In closed response systems, students must choose one response from a limited number of possible responses presented on the screen. Students know exactly what they are allowed to say in response to any given prompt. By contrast, in systems with open response design, the network remains hidden and the student is challenged to generate the appropriate response without any cues from the system. Closed Response Designs. One of the first implementations of a closed response design was the Voice Interactive Language Instruction System (VILIS) developed at SRI (Bernstein & Rtischev, 1991). This system elicits spoken student responses by

presenting queries about graphical displays of maps and charts. Students infer the right answers to a set of multiple-choice questions and produce spoken responses. A more recent prototype currently under development in SRI is the Voice Interactive Language Training System (VILTS), a system designed to foster speaking and listening skills for beginning through advanced L2 learners of French (Egan, 1996; Neumeyer et al., 1996; Rypa, 1996). The system incorporates authentic, unscripted conversational materials collected from French speakers into an engaging, flexible, and user-centered lesson architecture. The system deploys speech recognition to guide students through the lessons and automatic pronunciation scoring to provide feedback on the fluency of student responses. As far as

we know, only the pronunciation scoring aspect of the system has been validated in experimental trials (Neumeyer et al., 1996). In pedagogically more sophisticated systems, the query-response mode is highly contextualized and presented as part of a simulated conversation with a virtual interlocutor. To stimulate student interest, closed response queries are often presented in the form of games or goal-driven tasks. One commercial system that exploits the full potential of this design is TraciTalk (Courseware Publishing International, Inc., Cupertino, CA), a voice-driven multimedia CALL system aimed at more advanced ESL learners. In a series of loosely connected scenarios, the system engages students in solving a mystery. Prior to each scenario, students are given a task (e.g.,

eliciting a certain type of information), and they accomplish this task by verbally interacting with characters on the screen. Each voice interaction offers several possible responses, and each spoken response moves the conversation in a slightly different direction. There are many paths through each scenario, and not every path yields the desired information. This motivates students to return to the beginning of the scene and try out a different interrogation strategy. Moreover, TraciTalk features an agent that students can ask for assistance and accepts spoken commands for navigating the system. Apart from being more fun and interesting, games and task-oriented programs implicitly provide positive feedback by giving students the feeling of having solved a problem solely by

communicating in the target language. The speech recognition technology underlying closed response query implementations is very simple, even in the more sophisticated systems. For any given interaction, the task perplexity is low and the vocabulary size is comparatively small. As a result, these systems tend to be very robust. Recognition accuracy rates in the low to upper 90% range can be expected depending on task definition, vocabulary size, and the degree of non-native disfluency. FUTURE TRENDS IN VOICE-INTERACTIVE CALL In the previous sections, we reviewed the current state of speech technology, discussed some of the factors affecting recognition performance, and introduced a number of research prototypes that illustrate the range of speech-enabled CALL applications that