Based on the results of psycholinguistic experiments, the perceptual magnet effect is the important factor in speech development. This effect produced a warped auditory space to the corresponding phoneme. The purpose of this study was to develop a neural network model in simulation of speech perception. The neural network model with unsupervised learning was used to determine the phonetic categories of phoneme according to the formant frequencies of the vowels. The modified Self-Organizing Map (SOM) algorithm was proposed to produce the auditory perceptual space of English vowels. Simulated results were compared with findings from psycholinguistic experiments, such as categorization of English /r/ and /l/ and prototype and non-prototype vowels, to indicate the model's ability to produce auditory perception space. In addition, this speech perception model was combined with the neural network model (Directions into Velocities Articulator, DIVA) to simulate categorization of ten English vowels and their productions to show the learning capability of speech perception and production. We further extended this modified DIVA model to show its capability to categorize six Chinese vowels (/a/, /i/, /u/, /e/, /o/, /y/) and their productions. Finally, this study proposed further development and related discussions for this speech perception model and its clinical application.
|Journal||Proceedings of Meetings on Acoustics|
|State||Published - 2013|
|Event||21st International Congress on Acoustics, ICA 2013 - 165th Meeting of the Acoustical Society of America - Montreal, QC, Canada|
Duration: 2 Jun 2013 → 7 Jun 2013