A New Statistic Feature of the Short-Time Amplitude Spectrum Values for Human's Unvoiced Pronunciation
This work addresses a specific problem in speech signal processing by providing evidence for a foundational hypothesis, but it is incremental as it builds on existing statistical models without broad application beyond this domain.
The paper discovered a new statistical feature in the short-time amplitude spectrum of unvoiced speech signals, revealing relationships between amplitude averages and standard deviations across frequencies, and proposed a model supported by this feature to validate the hypothesis of identical amplitude distribution for all frequencies.
In this paper, a new statistic feature of the discrete short-time amplitude spectrum is discovered by experiments for the signals of unvoiced pronunciation. For the random-varying short-time spectrum, this feature reveals the relationship between the amplitude's average and its standard for every frequency component. On the other hand, the association between the amplitude distributions for different frequency components is also studied. A new model representing such association is inspired by the normalized histogram of amplitude. By mathematical analysis, the new statistic feature discovered is proved to be necessary evidence which supports the proposed model, and also can be direct evidence for the widely used hypothesis of "identical distribution of amplitude for all frequencies".