Supervector pre-processing for PRSVM-based Chinese and Arabic dialect identification

Phonotactic modeling has become a widely used means for speaker, language, and dialect recognition. This paper explores variations to supervector pre-processing for phone recognition-support vector machines (PRSVM) based dialect identification. The aspects studied are: (i) normalization of supervector dimensions in the pre-squashing stage, (ii) impact of alternative squashing functions, and (iii) N-gram selection for supervector dimensionality reduction. In (i) and (ii), we find that several alternatives to commonly used approaches can provide moderate, yet consistent performance improvements. In (iii), a newly proposed dialect salience measure is applied in supervector dimension selection and compared to a common N-gram frequency based selection. The results show a strong correlation between dialect-salience and frequency of occurrence in N-grams. The evaluations in this study are conducted on a corpus of Chinese dialects, a Pan-Arabic corpus, and a set of Arabic CTS corpora.