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To examine the effects of stimulus structure and variability on perceptual learning, we compared transcription accuracy before and after training with synthetic speech produced by rule. Subjects were trained with either isolated words or fluent sentences of synthetic speech that were either novel stimuli or a fixed list of stimuli that was repeated. Subjects who were trained on the same stimuli every day improved as much as did the subjects who were given novel stimuli. In a second experiment, the size of the repeated stimulus set was reduced. Under these conditions, subjects trained with repeated stimuli did not generalize to novel stimuli as well as did subjects trained with novel stimuli. Our results suggest that perceptual learning depends on the degree to which the training stimuli characterize the underlying structure of the full stimulus set. Furthermore, we found that training with isolated words only increased the intelligibility of isolated words, although training with sentences increased the intelligibility of both isolated words and sentences.Speech signals provide an especially interesting and important class of stimuli for studying the effect of stimulus variability on perceptual learning, primarily because of the lack of acoustic-phonetic invariance of the speech signal (e.g., Liberman, Cooper, Shankweiler, & Studdert-Kennedy, 1967). Despite large differences in the acoustic-phonetic structure of speech produced by different talkers, listeners seldom have any difficulty recognizing the speech produced by a novel talker. Although context-dependent and talker-dependent acoustic-phonetic variability has often been viewed as noise that must be stripped away from the speech signal in order to reveal invariant phonetic structures (e.g., Stevens & Blumstein, 1978), it is also possible that this variability serves as an important source of information for the listener, which indicates structural relations among acoustic cues as well as information about the talker . If the sources of variability in the speech waveform are understood by the listener, this information may play an important role in the perceptual decoding of linguistic segments (see Liberman, 1970). Therefore, if a listener must learn to recognize speech that is either degraded or impoverished, information about acoustic-phonetic variability of the speech signal may be critical to the learning process. Schwab, Nusbaum, and Pisoni (1985) recently demonstrated that moderate amounts of training with low-intelligibility synthetic speech will improve word recognition performance for novel stimuli generated by the same text-to-speech system. Schwab et al. trained subjects by presenting synthetic speech followed by immediate feedback in recognition tasks for words in isolation, for words in fluent meaningful sentences, and for words in fluent semantically anomalous sentences. Subjects trained under these conditions improved significantly in recognition performance for synthetic words in isolation and in sentence contexts compared to subjec...

Figure 1. Exemplars of Human-Computer Integration: extending the body with additional robotic arms; [70] embedding computation into the body using electric muscle stimulation to manipulate handwriting [48]; and, a tail extension controlled by body movements [86].

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