s, there are sounds in Chinese that do not resemble any sounds in English.
A system for teaching the pronunciation of Japanese long vowels, the mora nasal, and mora obstruents was recently built at the University of Tokyo. This system enables students to practice phonemic differences in Japanese that are known to present special challenges to L2 learners. It prompts students to pronounce minimal pairs (eg, long and short vowels) and returns immediate feedback on segment duration. Based on the limited data, the system seems quite effective at this particular task. Learners quickly mastered the relevant duration cues, and the time spent on learning these pronunciation skills was well within the constraints of Japanese L2 curricula (Kawai & Hirose, 1997). However, the study provides no data on long-term effects of using the system.
Supra-segmental Feedback. Correct usage of supra-segmental features such as intonation and stress has been shown to improve the syntactic and semantic intelligibility of spoken language (Crystal, 1981). In spoken conversation, intonation and stress information not only helps listeners to locate phrase boundaries and word emphasis, but also to identify the pragmatic thrust of the utterance (eg, interrogative vs. declarative). One of the main acoustical correlates of stress and intonation is fundamental frequency (F0); other acoustical characteristics include loudness, duration, and tempo. Most commercial signal processing software have tools for tracking and visually displaying F0 contours (see Figure 2). Such displays can and have been used to provide valuable pronunciation feedback to students. Experiments have shown that a visual F0 display of supra-segmental features combined with audio feedback is more effective than audio feedback alone (de Bot, 1983; James, 1976), especially if the student's F0 contour is displayed along with a native model. The feasibility of this type of visual feedback has been demonstrated by a number of simple prototypes (Abberton & Fourcin, 1975; Anderson-Hsieh, 1994; Hiller et al., 1994; Spaai & Hermes, 1993; Stibbard, 1996). We believe that this technology has a good potential for being incorporated into commercial CALL systems.
Other types of visual pronunciation feedback include the graphical display of a native speaker's face, the vocal tract, spectrum information, and speech waveforms (see Figure 2). Experiments have shown that a visual display of the talker improves not only word identification accuracy (Bernstein & Christian, 1996), but also speech rhythm and timing (Markham & Nagano-Madesen, 1997). A large number of commercial pronunciation tutors on the market today offer this kind of feedback. Yet others have experimented with using a real-time spectrogram or waveform display of speech to provide pronunciation feedback. Molholt (1990) and Manuel (1990) report anecdotal success in using such displays along with guidance on how to interpret the displays to improve the pronunciation of suprasegmental features in L2 learners of English. However, the authors do not provide experimental evidence for the effectiveness of this type of visual feedback. Our own experience with real-time spectrum and waveform displays suggests their potential use as pronunciation feedback 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 p...
