Voice recognition method and device and device for voice recognition
A speech recognition and speech technology, applied in speech recognition, speech analysis, instruments, etc., can solve problems such as low speech recognition efficiency
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Embodiment 1
[0069] refer to Figure 4 , shows a flow chart of the steps of an embodiment of a training method for a neural network model according to an embodiment of the present invention. The neural network model is applied to speech recognition, and the above-mentioned acoustic model for speech recognition specifically includes: the above-mentioned neural network model and hidden Mark Husband model; the method specifically includes the following steps:
[0070] Step 401. Align the training data to obtain alignment information;
[0071] Step 402, according to the above-mentioned alignment information, divide the above-mentioned training data into data blocks of a preset length;
[0072] Step 403, according to the data blocks corresponding to the above training data, the above neural network model is trained; the above neural network model may include: an input layer, a hidden layer and an output layer; the above hidden layer may include: a feedforward neural network layer and a self-at...
Embodiment 2
[0088] refer to Figure 5 , which shows a flow chart of the steps of an embodiment of a voice recognition method in an embodiment of the present invention, the method specifically includes the following steps:
[0089] Step 501, determining the speech characteristics of the speech to be recognized;
[0090] Step 502, using an acoustic model to determine the speech recognition result corresponding to the above-mentioned speech features; the above-mentioned acoustic model may include: a neural network model and a hidden Markov model; the above-mentioned neural network model may include: an input layer, a hidden layer, and an output layer; the above-mentioned The hidden layer may include: a feedforward neural network layer and a self-attention neural network layer;
[0091] Step 503 , outputting the above speech recognition result.
[0092] Embodiments of the present invention can Figure 4 The resulting neural network model is used in the speech recognition process.
[0093] I...
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