Automatic Recognition of Slovak Regional Dialects

Regional and foreign accents are known to cause problems in many areas of the automatic speech processing, such as speech recognition, speaker recognition, emotion or stress detection. To enable using regional-accent-specific acoustic and language models, the accent of the speaker has to be identified. In this work a preliminary analysis is done on the distinguishability of the Slovak dialects by a basic automatic classifier. In order to determine the range of differences between the Slovak dialects, to estimate the possible extreme values of recognition rates with a given recognizer, the experiments were done on the recordings of speakers using the authentic regional dialects, not just a slightly local-accented standard pronunciation. The reliability of dialect identification, of course, depends greatly on the definition of dialectal areas and on the choice and representativeness of the recordings used for training. We introduce The Sound Archive of the Slovak Dialects. From this collection, we have chosen an appropriate set of recordings and created a database, for dialect-specific acoustic models training and testing. A model of the standard Slovak needed for comparison was trained on the recordings of the Slovak Parliament. A standard Gaussian Mixture Model based recognizer was used for the classification experiments. The results have proven the ability of the GMM classifier to identify the three macro-dialect groups and standard Slovak with reasonable reliability which is already high enough for practical applications. However even better results could be achieved with a bigger volume of suitable representative training data, with gender-dependent modeling, additional features representing prosody, as well as introducing deep-learning modeling techniques, which we consider as future steps.