Home voiceprint recognition method based on CNN classification and feature matching combination
A voiceprint recognition and feature matching technology, applied in character and pattern recognition, speech analysis, instruments, etc., can solve the problems of high false detection rate and missed detection rate, and achieve high and low false detection rate and missed detection rate False detection rate and missed detection rate to ensure absolute safety
Pending Publication Date: 2020-10-16
NANJING UNIV OF POSTS & TELECOMM
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This method improves the existing model to solve the problem of high false detection rate and missed detection rate
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[0027] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.
[0028] Such as figure 1 As shown, the present invention provides a voiceprint recognition method for household CNN classification and feature matching, comprising the following steps:
[0029] 1) Preprocessing the input speaker's voice, including sampling and quantization, pre-emphasis, windowing and framing, endpoint detection, etc. The purpose of preprocessing is to eliminate the interference of vocal organs and voice acquisition equipment, and improve the recognition...
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The invention discloses a home voiceprint recognition method based on CNN classification and feature matching combination. The method comprises the following steps: performing short-time Fourier transform on voice to generate a speech spectrogram; inputting the speech spectrogram into a trained convolutional neural network for classification, and if the spectrogram is identified as a non-family member, ending the process; extracting MFCC characteristic parameters of the voice signals; and matching the MFCC characteristic parameters with a k-means characteristic template to obtain a final identification result. Based on the generation of the spectrogram, the convolutional neural network, the k-means algorithm and the cosine similarity measurement method, the false detection rate and the missing detection rate of speech recognition are effectively reduced under the condition of ensuring the recognition accuracy, the problems of relatively high false detection rate and missing detection rate are solved, and the absolute safety of a home environment is ensured.
Description
technical field [0001] The invention belongs to the field of voiceprint recognition, and in particular relates to a voiceprint recognition method oriented to the combination of CNN classification and feature matching for home furnishing. Background technique [0002] Voiceprint recognition is also called speaker recognition, including speaker identification and speaker confirmation. Voiceprint recognition has a wide range of applications, including financial fields, military security, medical fields, and home security fields. Before the recognition of many voiceprint recognition systems, in addition to preprocessing operations, feature parameters and model matching are crucial to the accuracy of recognition. The existing voiceprint recognition algorithm cannot achieve 100% recognition accuracy, and the false detection rate and missed detection rate are high, which cannot guarantee the absolute safety of personal and property in the home environment. Contents of the invent...
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IPC IPC(8): G10L17/18G10L25/24G10L25/03G06K9/62
CPCG10L17/18G10L25/24G10L25/03G06F18/23213
Inventor 张晖张金鑫赵海涛孙雁飞倪艺洋朱洪波
Owner NANJING UNIV OF POSTS & TELECOMM