Wakeup word detection method, device and equipment based on artificial intelligence, and medium

Abstract

The application discloses a wakeup word detection method, device and equipment based on artificial intelligence, and a storage medium thereof. The method comprises the following steps of acquiring to-be-identified voice data, and extracting voice characteristics of each voice frame in the to-be-identified voice data; inputting the voice characteristics into a preconstructed deep neural network model, wherein the output voice characteristics are corresponding to posterior probability vectors of syllable identification, and the deep neural network model comprises syllable output units, and the number of the syllable output units is same as that of syllable of a preconstructed pronunciation dictionary; determining a target probability vector from the posterior probability vectors according toa syllable combination sequence, wherein the syllable combination sequence is constructed based on the input wakeup word text; and calculating the credibility according to the target probability vector, and determining that the voice frame comprises the wakeup word text when the credibility is greater than a threshold. Through the scheme provided by the embodiment of the application, the calculation complexity is low, the response speed is fast, an operation of performing special optimization improvement on the fixed wakeup word is avoided, and the wakeup detection efficiency is effectively improved.

Description

technical field [0001] The present application generally relates to the technical field of speech recognition, and in particular relates to an artificial intelligence-based wake-up word detection method, device, equipment and medium. Background technique [0002] The key technologies of speech technology (Speech Technology) include automatic speech recognition technology (ASR) and speech synthesis technology (TTS) and voiceprint recognition technology. Enabling computers to hear, see, speak, and feel is the development direction of human-computer interaction in the future, and voice will become one of the most promising ways of human-computer interaction in the future. Apply voice technology to electronic equipment to realize the function of waking up electronic equipment, that is, voice wake-up technology. Usually the voice wake-up (KeyWord Spotting, KWS) is to set a fixed wake-up word. After the user speaks the wake-up word, the voice recognition function on the terminal ...

AI Technical Summary

Problems solved by technology

[0003] In order to meet the needs of users for customizing the wake-up word, there are also custom-based wake-up schemes in the prior art, such as a custom wake-up word scheme based on a hidden Markov model (Hidden Markov Model, HMM), which includes an acoustic model and There are two parts of the HMM decoding network. In the process of wake-up word detection, the fixed window size of the voice call is input into the decoding network, and then the Viterbi decoding algorithm is used to find the optimal decoding path. It has the disadvantages of large amount of calculation and slow response delay.

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