Wake-up method, device and equipment for intelligent equipment
A smart device and sample technology, applied in the direction of program control device, program control design, voice analysis, etc., can solve the problems of high resource occupation and poor wake-up effect, and achieve the effect of reducing dependence, improving performance, and reducing occupied resources
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Embodiment 1
[0126] An embodiment of the present disclosure provides a flowchart of a method for waking up a smart device, such as figure 2 shown, including:
[0127] Step S201, obtaining an audio data sequence;
[0128] Because in the solution of the embodiment of the present disclosure, the label predicted by the following wake-up model is the label corresponding to each audio data sequence, if the down-sampling process is performed, the wake-up result will not be affected, therefore, the above-mentioned audio data sequence can be down-sampled Processing, in order to achieve the effect of greatly reducing the amount of calculation.
[0129] As an optional implementation, after obtaining the audio data sequence, it also includes:
[0130] The above audio data sequence is down-sampled.
[0131] Wherein, the ratio of downsampling is specifically set according to specific implementation conditions.
[0132] Step S202, input the audio data sequence into the wake-up model, use the wake-up...
Embodiment approach 1
[0138] Embodiment 1: The sequence label is a sequence label that recognizes the wake-up sentence sequence or does not recognize the wake-up sentence sequence.
[0139] The specific manner of determining the sequence label in the above-mentioned embodiment 1 is as described in the above-mentioned step S102, and will not be repeated here.
[0140] It should be noted that, the content of the above-mentioned sequence label can be a substitute symbol for identifying a wake-up sentence sequence or not recognizing a wake-up sentence sequence. It can also be the specific content of the recognized sequence, for example, lan se ping guo.
Embodiment approach 2
[0141] Embodiment 2: The sequence labels are sequence labels and probabilities of recognized wake-up sentence sequences and sequence labels and probabilities of unrecognized wake-up sentence sequences.
[0142] According to the sequence characteristics of each wake-up statement sample in the forward sample set and the above-mentioned first sequence characteristics, predict and identify the first sequence label and first probability of the wake-up statement sequence;
[0143] According to the sequence characteristics of each non-awakening sentence sample in the reverse sample set and the above-mentioned first sequence characteristics, the second sequence label and the second probability of the wake-up sentence sequence are not recognized in the prediction mark;
[0144] According to the first sequence label and the first probability, the second sequence label and the second probability, predict whether the wake-up sentence sequence is recognized.
[0145] It should be noted tha...
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