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During spoken communication, the fine acoustic properties of human speech can reveal vital sociolinguistic and linguistic information about speakers and thus, these properties can function as reliable identification markers of speakers' identity. One key piece of information speech reveals is speakers' dialect. The first aim of this study is to provide a machine learning method that can distinguish the dialect from acoustic productions of sonorant sounds. The second aim is to determine the classification accuracy of dialects from the temporal and spectral information of a single sonorant sound and the classification accuracy of dialects using additional co-articulatory information from the adjacent vowel. To this end, this paper provides two classification approaches. The first classification approach aims to distinguish two Greek dialects, namely Athenian Greek, the prototypical form of Standard Modern Greek and Cypriot Greek using measures of temporal and spectral information (i.e., spectral moments) from four sonorant consonants /m n l r/. The second classification study aims to distinguish the dialects using coarticulatory information (e.g., formants frequencies F1 − F5, F0, etc.) from the adjacent vowel in addition to spectral and temporal information from sonorants. In both classification approaches, we have employed Deep Neural Networks, which we compared with Support Vector Machines, Random Forests, and Decision Trees. The findings show that neural networks distinguish the two dialects using a combination of spectral moments, temporal information, and formant frequency information with 81% classification accuracy, which is a 14% accuracy gain over employing temporal properties and spectral moments alone. In conclusion, Deep Neural Networks can classify the dialect from single consonant productions, making them capable of identifying sociophonetic shibboleths. |
