Dialect distance assessment method based on comparison of pitch pattern statistical models

Dialect variations of a language have a severe impact on the performance of speech systems. Therefore, knowing how close or diverse dialects are in a given language space provides useful information to predict, or improve, system performance when there is a mismatch between train and test data. Distance measures have been used in several applications of speech processing. However, apart from phonetic measures, little if any work has been done on dialect distance measurement. This study explores differences in pitch movement microstructure among dialects. A method of dialect distance assessment based on pitch patterns modeled progressively from pitch contour primitives is proposed. The presented method does not require any manual labeling and is text-independent. The KL divergence is employed to compare the resulting statistical models. The proposed scheme is evaluated on a corpus of Arabic dialects, and shown to be consistent with the results from the spectral-based dialect classification system. Finally, it is also shown using a perceptive evaluation that the proposed objective approach correlates well with subjective distances.