A model of speech segmentation in a stress language is proposed, according to which the occurrence of a strong syllable triggers segmentation of the speech signal, whereas occurrence of a weak syllable does not trigger segmentation. We report experiments in which listeners detected words embedded in nonsense bisyllables more slowly when the bisyllable had two strong syllables than when it had a strong and a weak syllable; mint was detected more slowly in mintayve than in minlesh. According to our proposed model, this result is an effect of segmentation: When the second syllable is strong, it is segmented from the first syllable, and successful detection of the embedded word therefore requires assembly of speech material across a segmentation position. Speech recognition models involving phonemic or syllabic receding, or based on strictly left-to-right processes, do not predict this result. It is argued that segmentation at strong syllables in continuous speech recognition serves the purpose of detecting the most efficient locations at which to initiate lexical access. Speech recognition is the process by which meaning is derived from the acoustic signal. A recognizer (be it a human or a machine) keeps in its memory a set of discrete meanings
Stored only when both papers are in the corpus — 1,060 citations exist upstream.
Imported from OpenAlex (CC0) — nothing here is generated.