Suchergebnisse

Past research has demonstrated that there are cognitive processing costs associated with comprehension of speech generated by text-to-speech synthesizers, relative to comprehension of natural speech. This finding has important performance implications for the many applications that use such systems. The purpose of this study was to ascertain whether certain characteristics of synthetic speech slow on-line, real-time cognitive processing. Whereas past research has focused on the phonemic acoustic structure of synthetic speech, we manipulated prosodic, syntactic, and semantic cues in a task requiring participants to recall sentences spoken either by a human or by one of two speech synthesizers. The findings were interpreted to suggest that inappropriate prosodic modeling in synthetic speech was the major source of a performance differential between natural and synthetic speech. Prosodic cues, along with others, guide the parsing of speech and provide redundancy. When these cues are absent or inaccurate, the additional burden placed on working memory may exceed its capacity, particularly in time-limited, demanding tasks. Actual or potential applications of this research include improvement of text-to-speech output systems in warning systems, feedback devices in aerospace vehicles, educational and training modules, aids for the handicapped, consumer products, and technologies designed to increase the functional independence of older adults.

This research investigated the differential impact of synthetic voice quality and text difficulty on comprehension of extended prose. Sixty participants listened to five easy and five difficult passages in one of three speech modes: natural speech, VOTRAX (low intelligibility), or DECtalk (high intelligibility). Comprehension of DECtalk was equal to that of natural speech, whereas comprehension of VOTRAX was significantly poorer than with natural speech or DECtalk. Subjects were also asked to shadow passages of each speech type as a measure of resource processing demands. It was found that shadowing accuracy was significantly better for natural speech than for DECtalk and shadowing of DECtalk was markedly superior to that of VOTRAX. The results of this study suggest that resource-demand measures alone may not be appropriate to predict performance in practical applications. Specifically, overall comprehension may not suffer despite on-line losses in processing. These findings also point to a differential allocation of cognitive resources by speech synthesizers of differing intelligibility.