ABSTRACT
We present a complexity measure for any finite time series. This measure has invariance under any monotonic transformation of the time series, has a degree of robustness against noise, and has the adaptability of satisfying almost all the widely accepted but conflicting criteria for complexity measurements. Surprisingly, the measure is developed from Kolmogorov complexity, which is traditionally believed to represent only randomness and to satisfy one criterion to the exclusion of the others. For familiar iterative systems, our treatment may imply a heuristic approach to transforming symbolic dynamics into permutation dynamics and vice versa.
ABSTRACT
OBJECTIVE: To analyze the speech characters with computation complexity. METHOD: Voices of 51 testees spoke the same paragraph were recorded and the same sentence of voice waveform was intercepted as source data. There were two kinds of sample voices: same testee speaking the same sentence at different time and different testee speaking the same sentence. RESULT: The computation complexity curves of the different testee were obviously distinguishing, while those of the same testee were almost the same. In a 2D embedded space the computation complexity features of individual testee differs with others even if testees speak the same sentences. CONCLUSION: This complexity features might be applied to speaker recognition system and using complexity method to analyze speech signals has wide application prospect.
