Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : A large vocabulary continuous speech recognition system for Persian language | Sameti et al. EURASIP Journal on Audio Speech and Music Processing 2011 2011 6 http content 2011 1 6 D EURASIP Journal on Audio Speech and Music Processing a SpringerOpen Journal RESEARCH Open Access A large vocabulary continuous speech recognition system for Persian language Hossein Sameti Hadi Veisi Mohammad Bahrani Bagher Babaali and Khosro Hosseinzadeh Abstract The first large vocabulary speech recognition system for the Persian language is introduced in this paper. This continuous speech recognition system uses most standard and state-of-the-art speech and language modeling techniques. The development of the system called Nevisa has been started in 2003 with a dominant academic theme. This engine incorporates customized established components of traditional continuous speech recognizers and its parameters have been optimized for real applications of the Persian language. For this purpose we had to identify the computational challenges of the Persian language especially for text processing and extract statistical and grammatical language models for the Persian language. To achieve this we had to either generate the necessary speech and text corpora or modify the available primitive corpora available for the Persian language. In the proposed system acoustic modeling is based on hidden Markov models and optimized decoding pruning and language modeling techniques were used in the system. Both statistical and grammatical language models were incorporated in the system. MFCC representation with some modifications was used as the speech signal feature. In addition a VAD was designed and implemented based on signal energy and zero-crossing rate. Nevisa is equipped with out-of-vocabulary capability for applications with medium or small vocabulary sizes. Powerful robustness techniques were also utilized in the system. Model-based approaches like PMC MLLR and MAP along with feature robustness methods such as CMS PCA RCC and VTLN and speech .
