Thirty years of research has uncovered the broad principles that characterize spoken word processing across listeners. However, there have been few systematic investigations of individual differences. Such an investigation could help refine models of word recognition by indicating which processing parameters are likely to vary, and could also have important implications for work on language imp...

In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...

We used eyetracking to examine how tonal versus segmental information influence spoken word recognition in Mandarin Chinese. Participants heard an auditory word and were required to identify its corresponding picture from an array that included the target item (chuang2 ‘bed’), a phonological competitor (segmental: chuang1 ‘window’; cohort: chuan2 ‘ship’; rhyme: huang2 ‘yellow’; tonal: niu2 ‘cow...

Theories of spoken word-recognition differ in the claims they make about continuous and discontinuous mapping of the speech input onto mental representations of lexical form. The research reported here contrasts the claims of a discontinuous, prefix-stripping model, and of a cohort-based continuous access model. In three experiments, using the gating, auditory lexical decision, and auditory nam...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...