Training method and using method of voice wake-up hybrid model and related equipment

A hybrid model and voice wake-up technology, applied in voice analysis, voice recognition, neural learning methods, etc., can solve the problems of low wake-up rate and high complexity of voice wake-up technology

Active Publication Date: 2020-11-13
深圳市友杰智新科技有限公司
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Problems solved by technology

[0003] The main purpose of this application is to provide a training method, usage method and related equipment of a voice wake-up hybrid model, aiming to solve the drawbacks of the existing voice wake-up technology with high complexity and low wake-up rate

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  • Training method and using method of voice wake-up hybrid model and related equipment
  • Training method and using method of voice wake-up hybrid model and related equipment
  • Training method and using method of voice wake-up hybrid model and related equipment

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[0067] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0068] refer to figure 1 , an embodiment of the present application provides a method for training a voice-awakened hybrid model, including:

[0069] S1: Acquire a preprocessing sample set, the preprocessing sample set includes a plurality of clean wake-up samples and noisy samples;

[0070] S2: input the log power spectrum feature of each described clean wake-up sample and each described noisy sample into the speech separation network, obtain the first loss function and separate output;

[0071] S3: Using a feature transformation network to perform feature extraction on...

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Abstract

The invention provides a training method and using method of a voice wake-up hybrid model and related equipment; during model training, a voice wake-up hybrid model is obtained through hybrid learningtraining of a voice separation network, a feature transformation network and a wake-up word detection network. When the model is used, an audio to be recognized is input into the voice wake-up hybridmodel, and the wake-up probability is directly obtained. When the wake-up probability is greater than the threshold, the wake-up word identified from a to-be-identified audio is judged. In a model training process, a first loss function obtained through the voice separation network and a second loss function obtained through the wake-up word detection network are weighted to obtain a comprehensive loss function, and weight parameters of the voice separation network, the feature transformation network and the wake-up word detection network are obtained through back propagation and learning according to the comprehensive loss function. Meanwhile, the networks are unified into one framework, and a joint optimization method is used, so that the model can learn optimal separation and wake-up network parameters at the same time, and the wake-up rate is effectively improved.

Description

technical field [0001] The present application relates to the technical field of voice wake-up, and in particular to a training method, usage method and related equipment of a voice wake-up hybrid model. Background technique [0002] In the existing voice wake-up technology, there is a method based on the traditional GMM-HMM (Gaussian Mixture Model-Hidden Markov Model), which uses monophone or triphone as the hidden state of HMM (Hidden Markov Model), and obtains the sound sequence After the signal, the optimal state sequence is obtained by decoding to judge whether there are target keywords in the voice signal. This method is complex to implement, and requires detailed labeling information for samples. The cost of data acquisition is high, and the effect is not ideal. Word rejection is poor; other end-to-end methods based on DNN (deep neural network) have a simpler implementation process and better results than traditional methods, but the models are generally too large to ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L15/02G10L15/06G10L15/14G10L15/16G10L15/22G10L25/78G06N3/08
CPCG06N3/084G10L15/02G10L15/063G10L15/142G10L15/16G10L15/22G10L25/78G10L2015/223
Inventor 王维王广新太荣鹏
Owner 深圳市友杰智新科技有限公司
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