Informatica

Conventional large vocabulary automatic speech recognition (ASR) systems require a mapping from words into sub-word units to generalize over the words that were absent in the training data and to enable the robust estimation of acoustic model parameters. This paper surveys the research done during the last 15 years on the topic of word to sub-word mappings for Lithuanian ASR systems. It also compares various phoneme and grapheme based mappings across a broad range of acoustic modelling techniques including monophone and triphone based Hidden Markov models (HMM), speaker adaptively trained HMMs, subspace gaussian mixture models (SGMM), feed-forward time delay neural network (TDNN), and state-of-the-art low frame rate bidirectional long short term memory (LFR BLSTM) recurrent deep neural network. Experimental comparisons are based on a 50-hour speech corpus. This paper shows that the best phone-based mapping significantly outperforms a grapheme-based mapping. It also shows that the lowest phone error rate of an ASR system is achieved by the phoneme-based lexicon that explicitly models syllable stress and represents diphthongs as single phonetic units.

This paper presents the corpus-driven approach in building the computational model of fundamental frequency, or ${\mathit{F}}_0$, for Lithuanian language. The model was obtained by training the HMM-based speech synthesis system HTS on six hours of speech coming from multiple speakers. Several gender specific models, using different parameters and different contextual factors, were investigated. The models were evaluated by synthesizing ${\mathit{F}}_0$ contours and by comparing them to the original ${\mathit{F}}_0$ contours using criteria of root mean square error (RMSE) and voicing classification error. The HMM-based models showed an improvement of the RMSE over the mean-based model that predicted ${\mathit{F}}_0$ of the vowel on the basis of its average normalized pitch.