Voice activity detection system based on fundamental frequency and calculation method thereof

Abstract

The invention relates to a voice activity detection system based on the fundamental frequency and a calculation method thereof. All possible positions where a fundamental frequency appears are acquired through a fundamental frequency extraction algorithm, and the fundamental frequency is determined by cost. However, because the fundamental frequency may be interfered by low-frequency noise, whether a point is voice is judged by aid of the fact that a position where a fundamental frequency appears has a harmonic structure. Meanwhile, the speed of voice activity detection is increased and the detection accuracy is improved according to adaption of background energy. According to the voice activity detection system based on the fundamental frequency and the calculation method thereof of the invention, the voice activity detection system is enabled to have high robustness under the condition of low signal-to-noise ratio. When noise is difficult to distinguish in a time domain, the method enables noise to be correctly distinguished in a frequency domain according to significantly different characteristics of spectral distribution of noise signals and voice signals from time-domain distribution. The method can be widely applied to the field of voice signal processing.

Description

technical field [0001] The invention relates to an endpoint detection technology of a voice signal, in particular to an endpoint detection technology of a voice signal based on a fundamental frequency. Background technique [0002] The main purpose of the endpoint detection technology (Voice Activity Detection) is to detect a segment containing a voice signal from a given input voice signal, and give its start and end points. In recent years, with the development of computers, speech has gradually become the main way of human-computer interaction. Endpoint detection technology plays an important role in speech recognition, speech analysis and semantic understanding. A better speech endpoint detection result is very important to improve the accuracy of speech recognition and processing speed. [0003] Currently, endpoint detection techniques include methods such as time-domain energy, speech correlation, frequency-domain entropy, and model matching. These methods can achieve...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is how to provide an endpoint detection calculation method, so that the endpoint detection system has high robustness, and can maintain high detection accuracy even in the case of poor signal-to-noise ratio

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