Early L2 Spoken Word Recognition Combines Input-Based and Knowledge-Based Processing.

Abstract

This study examines the perceptual trade-off between knowledge of a language's statistical regularities and reliance on the acoustic signal during L2 spoken word recognition. We test how early learners track and make use of segmental and suprasegmental cues and their relative frequencies during non-native word recognition. English learners of Mandarin were taught an artificial tonal language in which a tone's informativeness for word identification varied according to neighborhood density. The stimuli mimicked Mandarin's uneven distribution of syllable+tone combinations by varying syllable frequency and the probability of particular tones co-occurring with a particular syllable. Use of statistical regularities was measured by four-alternative forced-choice judgments and by eye fixations to target and competitor symbols. Half of the participants were trained on one speaker, that is, low speaker variability while the other half were trained on four speakers. After four days of learning, the results confirmed that tones are processed according to their informativeness. Eye movements to the newly learned symbols demonstrated that L2 learners use tonal probabilities at an early stage of word recognition, regardless of speaker variability. The amount of variability in the signal, however, influenced the time course of recovery from incorrect anticipatory looks: participants exposed to low speaker variability recovered from incorrect probability-based predictions of tone more rapidly than participants exposed to greater variability. These results motivate two conclusions: early L2 learners track the distribution of segmental and suprasegmental co-occurrences and make predictions accordingly during spoken word recognition; and when the acoustic input is more variable because of multi-speaker input, listeners rely more on their knowledge of tone-syllable co-occurrence frequency distributions and less on the incoming acoustic signal.