Technique for Phoneme Set Selection for Automatic Russian Speech Recognition

In the paper, selection of best phoneme set for Russian automatic speech recognition is described. For the acoustic modeling, we describe a method based on combination of knowledge-based and statistical approaches to create several different phoneme sets. Applying this method to the Russian phonetic set of the IPA (International Phonetic Alphabet) alphabet, we first reduced it to 47 phonological units and derived several other phoneme sets with different number of phonological units from 27 till 47. Speech recognition experiments using these sets showed that reduced phoneme sets are better for phoneme recognition task and as good for word level speech recognition. For experiment with extra-large vocabulary, we used syntactico-statistical language model, which allowed us to achieve the word recognition accuracy of 73.1%. The results correspond to continuous Russian speech recognition quality obtained by other organizations up to date.