Skip to main content
Download PDF
- Main
Distribution and frequency: Modelling the effects of speaking rate o n category boundaries using a recurrent neural network
Abstract
We describe a recurrent neural network model of rate effects on the syllable-initial voicing distinction, specified by voiceonset-time (VOT). The stimuli were stylized /bi/ and /pi/ syllables covarying in VOT and syllable duration. Network performance revealed a systematic rate effect: as syllable duration increases, the category boundary moves toward longer VOT values, mirroring human performance. Two factors underlie this effect: the range of training stimuli with each VOT and syllable duration, and their frequency of occurrence. The latter influence was particularly strong, consistent with exemplar-based accounts of human category formation.
Main Content
For improved accessibility of PDF content, download the file to your device.
If you recently published or updated this item, please wait up to 30 minutes for the PDF to appear here.
Enter the password to open this PDF file:
File name:
-
File size:
-
Title:
-
Author:
-
Subject:
-
Keywords:
-
Creation Date:
-
Modification Date:
-
Creator:
-
PDF Producer:
-
PDF Version:
-
Page Count:
-
Page Size:
-
Fast Web View:
-
Preparing document for printing…
0%