ANN-MLP Classifier of Native and Nonnative Speakers Using Speech Rhythm Cues

摘要

In this paper, speech rhythm metrics were used in classification of native vs. nonnative speakers. The speech corpus exploited is a part of West Point corpus. Nonnative speakers (14) are English participants who read the same set of Arabic text then their Arabic counterpart (15). Seven rhythm metrics from all vowels and consonants were calculated from 145 sentences using two rhythm models: Interval Measures (IM) and Compensation/Control Index (CCI). Rhythm data were use as input vector of ANN-MLP classifier. The classifier was trained and tested using different configurations of the input vectors. The best accuracy of the engine achieved (80.7%) when we used all speech rhythm input vectors.