Real-time Segmentation Algorithm Of Speech Signal Based On Granular Computing

Real-Time Multi-Parameter Speech Segmentation Algorithm has very important meaning in the field of Speech Signal Processing. This research tries to find an effective method combines various characteristic parameters of speech signal to detect the sharp variations in continuous speech signal. And we can carry out the segmentation by the sharp variations of speech signal.First, in analyzing several characteristic parameters in common use, this paper presents a real time improvement algorithm, which processes in the window of 160 sample points including current 20 sample points frame and history 140 sample points, and based on the algorithm, this paper also present the Real-time Autocorrelation Speech Signal Segmentation Algorithm. Research illustrates the real time improved algorithm can process the real time speech signal. The characteristic parameters can characterize the sharp variations of speech signal. The real time improvement algorithm can segment various speech signal of signal noise ratio; the precision ratio of decisions reaches at 80 percent.Based on the realization of characteristic parameters distilling and analyzing of the granular computing theory, this paper applies it on the theology of speech signal segmentation and presents the Speech Signal Segmentation Algorithm Based on Granular Computing。This algorithm takes the advantage of the abilities on processing inaccurate, fuzzy, undefined, partial real and mass information to analyze eight characteristic parameters of speech signal, then gets the relationship between the characteristic parameters and the significance of the characteristic parameters, and finally forms the decision rules to segment speech signal efficiently. Research finds this algorithm can combine many kinds of characteristic parameters' benefits on speech segmentation. It can find almost all the segmentation points of speech signal by its generated decision rules; however, some missed and inaccuracy decisions also exist.To resolve the missed and inaccuracy arbitration problem, through a great deal of experimentations, this paper present the two improve methods of the Speech Signal Segmentation Algorithm Based on Granular Computing: First, improves the characteristic sampling process in order to eliminate noise disturb and increase the precision of arbitration; Second, on the foundation of arbitration rule, adds autocorrelation and energy parameters and forms two-way arbitration rule, this improvement can assist the arbitration and reduce the missed decisions. After these improvements, the precision of arbitration reaches the 90 percent, the missed and inaccuracy decisions obviously decrease. In conclusion, the Speech Signal Segmentation Algorithm Based on Granular Computing is a relative perfect Real-Time Multi-Parameter Speech Segmentation Algorithm.