Phonotactic learning is a crucial aspect of phonological acquisition and has figured significantly in computational research phonology (Prince & Tesar 2004). However, one persistent challenge for this line inducing non-local co-occurrence patterns (Hayes Wilson 2008). The current study develops probabilistic phonotactic model based on the Strictly Piecewise class subregular languages (Heinz...

In this paper, we investigate the phonotactic patterns that correlate with gender in given names for Mandarin Chinese, a language is phonotactically quite different from English. We find many of phonological predictors English trend same direction Mandarin. also compare grammars female and male obtained Maximum-Entropy learning models, certain low acoustic frequency sounds imply largeness accor...

In the acquisition of a second language, numerous factors interact simultaneously, including the patterns of the first language (L1), the patterns of the target language (L2), universals of language acquisition, and the amount and type of exposure to the L2. English is learned as a second language in India, where the first languages of the English learners differ widely in the phonotactics of c...

This study explores a multilingual phonotactic approach to automatic language identification using Broadcast News data. The definition of a multilingual phoneset is discussed and an upper limit on the performance of the phonotactic approach is estimated by eliminating any degradation due to recognition errors. This upper bound is compared to automatic language identification based on a phonotac...

Stoianov, Nerbonne and Bouma (1998) trained Simple Recurrent Networks (SRNs) on graphotactics of Dutch monosyllabic words, overcoming shortcomings of previous implementations. The current report is a continuation of our earlier research, but using phonetic data representations instead of orthographic, that is, learning phonotactics. In addition, we conducted further analysis of neural network p...

The most accurate unsupervised word segmentation systems that are currently available (Brent, 1999; Venkataraman, 2001; Goldwater, 2007) use a simple unigram model of phonotactics. While this simplifies some of the calculations, it overlooks cues that infant language acquisition researchers have shown to be useful for segmentation (Mattys et al., 1999; Mattys and Jusczyk, 2001). Here we explore...