Strange, W. (2010). Automatic selective perception (ASP) of first and second language speech: A working model. Journal of Phonetics. In press.
Presentation: Belinda
Summary: Sarah
In this paper, the author attempted to develop a perception model that describes and predicts the processing of speech signals by L1 listeners and L2 learners. In the Automatic Selective Perception model, native adult listeners’ perception of speech is considered as an automatic process. It is rapid and robust primarily because native listeners are able to selectively extract cues or parameters that are of contrastive salience. Native listeners are usually in the phonological mode when perceiving speech, in which low-level acoustic details are largely ignored. The opposite of the phonological mode is the phonetic mode. In particular, it is attention-demanding and usually requires listeners to pay attention to context-dependent acoustic information during speech processing. The phonetic mode is greatly involved in L2 perception, especially at the beginning stage of learning a second language. The objective of second language learning, therefore, is to obtain the selective perception routines, as called by the author, and to automatize them.
As these two modes are crucially relevant to L1 and L2 speech perception, it is necessary to study and discuss them separately. In the paper, it was shown that this can be achieved by manipulating stimulus complexity and task demands. The author discussed a series of experiments conducted in her laboratory, which looked into these two factors in detail. Results showed that stimulus and task manipulation yields different perceptual results of L2 listeners. Broadly speaking, L2 listeners would alter their modes of perception in face of different stimuli or tasks. These findings not only demonstrate her model’s validity, but also have important theoretical implications. The author suggested that more neurological evidence and training studies could be incorporated for further development of her perception model.
