Improvements in Serbian Speech Recognition Using Sequence-Trained Deep Neural Networks

Abstract

This paper presents the recent improvements in Serbian speech recognition that were obtained by using contemporary deep neural networks based on sequence-discriminative training to train robust acoustic models. More specifically, several variants of the new large vocabulary continuous speech recognition (LVCSR) system are described, all based on the lattice-free version of the maximum mutual information (LF-MMI) training criterion. The parameters of the system were varied to achieve best possible word error rate (WER) and character error rate (CER), using the largest speech database for Serbian in existence and the best n-gram based language model made for general purposes. In addition to tuning the neural network itself (its layers, complexity, layer splicing etc.) other language-specific optimizations were explored, such as the usage of accent-specific vowel phoneme models, and its combination with pitch features to produce the best possible results. Finally, speech database tuning was tested as well. Artificial database expansion was made by modifying speech speed in utterances, as well as volume scaling in an attempt to improve speech variability.