![]() ![]() ![]() ![]() A new robustness method called PC-PMC was also proposed and incorporated 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 enhancement methods like spectral subtraction and Wiener filtering, along with their modified versions, were diligently implemented and evaluated in the system. Powerful robustness techniques were also utilized in the system. Nevisa is equipped with out-of-vocabulary capability for applications with medium or small vocabulary sizes. In addition, a VAD was designed and implemented based on signal energy and zero-crossing rate. MFCC representation with some modifications was used as the speech signal feature. Both statistical and grammatical language models were incorporated in the system. 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. 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. 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. This engine incorporates customized established components of traditional continuous speech recognizers and its parameters have been optimized for real applications of the Persian language. The development of the system, called Nevisa, has been started in 2003 with a dominant academic theme. This continuous speech recognition system uses most standard and state-of-the-art speech and language modeling techniques. The first large vocabulary speech recognition system for the Persian language is introduced in this paper. ![]()
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