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Segmenting speech into words

R A Cole, J Jakimik, W E Cooper

    The Journal of the Acoustical Society of America
    |April 1, 1980
    PubMed
    Summary
    This summary is machine-generated.

    Listeners detect mispronunciations faster in later syllables of words. Context influences word perception, with second-syllable errors being more readily identified than initial ones.

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    Area of Science:

    • Psycholinguistics
    • Auditory Perception
    • Speech Processing

    Background:

    • Listeners process continuous speech, segmenting acoustic signals into words.
    • Phonetic ambiguity in speech requires contextual cues for accurate word perception.

    Purpose of the Study:

    • To investigate how listeners perceive word segmentation from ambiguous acoustic stimuli.
    • To determine the effect of contextual information on the detection of mispronunciations.
    • To examine reaction times to mispronunciations based on their syllable position.

    Main Methods:

    • Four experiments used a listening task with phonetically ambiguous stimuli.
    • Listeners identified mispronunciations in sequences perceived as one or two words.
    • Context was manipulated using narrative themes (Exp 1) and grammatical cues (Exp 2).

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  • Reaction times to mispronunciations in different syllable positions were recorded.

    • Main Results:

    • Mispronunciations in the second syllable were detected significantly faster (approx. 300 ms) than in the first syllable (Exp 1).
    • Faster detection of mispronunciations occurred in second/third syllables compared to word-initial ones (Exp 2).
    • These findings were replicated with natural speech (Exp 3) and supported by a predictability manipulation (Exp 4).

    Conclusions:

    • Prior linguistic context significantly influences how acoustic signals are segmented into words.
    • Mispronunciations are detected more rapidly in later syllables, suggesting word recognition is initiated early.
    • Predictability of syllables impacts mispronunciation detection, supporting models of incremental word recognition.