Variable rate signal compression has found many applications where there is no serious limitation on delay and the signal parameters are not very susceptible to errors. Methods used to apply variable rate coding usually rely on the redundancies included in the signal.
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Variable rate signal compression has found many applications where there is no serious limitation on delay and the signal parameters are not very susceptible to errors. Methods used to apply variable rate coding usually rely on the redundancies included in the signal.
Such methods are different in final bit rate, quality of the synthetic signal and computational requirements. This paper presents a novel method for compression of speech signal in a variable scheme. Based on the known linear prediction method, a simple and efficient model is developed in which segments of the speech signal are classified as voiced or unvoiced using the innovative voiced and unvoiced cycle concept.
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In this paper a recognition system for Persian words is introduced which utilizes the local higher order of the log-polar image autocorrelation for feature extraction of Persian sub-words. This feature extraction technique brings up leads to a system robustness in cases More
In this paper a recognition system for Persian words is introduced which utilizes the local higher order of the log-polar image autocorrelation for feature extraction of Persian sub-words. This feature extraction technique brings up leads to a system robustness in cases of writing variations alteration like scaled or rotated handwritings. Also using the log-polar transform, the sub-word image sampling will be performed so that most of acquired samples will be centered in a certain area. The proposed method uses the discrete Hidden Markov’s Model (HMM) as a classifier. Furthermore a net of dictionaries were employed to increase the reliability and precision of the system output. Finally, the Iran-Shahr database is utilized to evaluate the system performance. Comparing the results of the proposed method and other previous methods, proves that a less sensitivity has been achieved by the proposed method about handwriting variations.
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