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End point detection method for voice without leading mute segment

A technology of endpoint detection and silent segment, applied in speech analysis, instruments, etc., can solve problems such as unavailability, endpoint errors, and performance degradation of double-threshold algorithms

Active Publication Date: 2016-08-03
DALIAN UNIV OF TECH
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Problems solved by technology

However, when the speech signal has no leading silence segment, the performance of the double-threshold algorithm degrades rapidly, because the threshold-based method needs to determine the threshold first, and assume that the first few frames of the signal are noise signals that do not contain speech
In the process of voice endpoint detection, there may be a piece of voice without a leading silence segment or a leading silence segment is relatively short, then the voice threshold will be set incorrectly, so if the voice signal does not meet this assumption, the predefined threshold will be unavailable Yes, endpoint detection will fail

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  • End point detection method for voice without leading mute segment
  • End point detection method for voice without leading mute segment
  • End point detection method for voice without leading mute segment

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Embodiment Construction

[0055] The present invention will be further described below in conjunction with accompanying drawing.

[0056] Such as figure 1 Shown, a kind of endpoint detection method of voice without leading silence section, comprises the following steps:

[0057] Step 1, adopting the LMS adaptive algorithm to filter the noisy speech, specifically including the following sub-steps:

[0058] (a), there is a speech signal s(n) and a noise signal source v 0 (n), it can be considered that the noise source v 0 (n) In the process of propagating to the human ear, a filter with a transfer function of H is passed through, and the output is v 1 The signal of (n) is superimposed on the speech signal to obtain a noisy speech d(n),

[0059] d(n)=s(n)+v 1 (n)(1)

[0060] (b), equipped with LMS adaptive filter from another close to the noise signal source v 0 It is filtered at (n), and the filtered signal is y(n), the LMS adaptive filter is used to simulate the filter whose transfer function in ...

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Abstract

The invention relates to an end point detection method for voice without a leading mute segment, and belongs to the technical field of voice signal processing. The method comprises the following steps that S1) an LMS adaptive algorithm is used to filter the voice with noise; 2) the de-noised voice is changed from the time domain to the frequency domain; 3) an MFCC parameter of each frame is calculated; 4) the spectral entropy of each frame is calculated; 5) FCM is used to classify voice signals; and 6) the average spectral entropy of each classification in the step 5) is calculated, and voice signals and noise signals are marked. According to the method of the invention, it is not required to set a threshold, and end point detection error caused by that the threshold is set wrongly can be avoided; and compared with a monitored clustering method via a neural network and the like, sample training is not needed, calculation is simple and rapid, and the method is conducive to design of a real-time voice recognition system subsequently.

Description

technical field [0001] The invention relates to a method for detecting an endpoint of a speech without a leading silent segment, and belongs to the technical field of speech signal processing. Background technique [0002] With the development of human-computer information interaction technology, speech recognition technology shows its importance. In speech recognition system, speech endpoint detection is one of the key technologies in speech recognition. Speech endpoint detection refers to finding the starting point and ending point of the speech part in the noisy continuous sound signal. Whether the endpoint detection is accurate or not will directly affect the performance of the speech recognition system. An effective endpoint detection method can not only detect voice endpoints correctly, but also reduce data processing time, save storage space and improve efficiency. [0003] Due to different requirements, such as calculation accuracy, algorithm complexity, robustnes...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L25/87G10L25/24
CPCG10L25/24G10L25/87
Inventor 董明张超
Owner DALIAN UNIV OF TECH
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