Building Medium-Vocabulary Isolated-Word Lithuanian HMM Speech Recognition System

Abstract

In this paper, the opening work on the development of a Lithuanian HMM speech recognition system is described. The triphone single-Gaussian HMM speech recognition system based on Mel Frequency Cepstral Coefficients (MFCC) was developed using HTK toolkit. Hidden Markov model's parameters were estimated from phone-level hand-annotated Lithuanian speech corpus. The system was evaluated on a speaker-independent ∼ 750 distinct isolated-word recognition task. Though the speaker adaptation and language modeling techniques were not used, the system was performing at 20% word error rate.