The influence of errors in phonetic annotations on performance of speech recognition system

Abstract

This paper deals with errors in acoustic training data and the influence on speech recognition performance. The training data can be prepared manually, automatically or by combination of these two. In all cases, some mislabeled phonemes can appear in phonetic annotations. We conducted series of experiments which simulate some common errors. The experiments deal with various amount of changes in phonetic annotations such as different types of changes in voicing of obstruents, random substitution of consonants or vowels and random deleting of phonemes. All experiments were done for Czech language using GlobalPhone speech data set and both Gaussian mixture models and deep neural networks were used for acoustic modeling. The results show that some amount of such errors in training data does not influence speech recognition accuracy. The accuracy is significantly influenced only by large amount of errors (more than 50%).