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US 12,658,182 B2Granted
Speech wake-up method, electronic device, and storage medium
AssigneeBeijing Baidu Netcom Science Technology Co., Ltd.
InventorsSaisai Zou, Lei Jia, Haifeng Wang
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Abstract
A speech wake-up method, an electronic device, and a storage medium are provided. The method includes: performing a word recognition on a speech to be recognized to obtain a wake-up word recognition result (S210); performing a syllable recognition on the speech to be recognized to obtain a wake-up syllable recognition result, in response to determining that the wake-up word recognition result represents that the speech to be recognized contains a predetermined wake-up word (S220); and determining that the speech to be recognized is a correct wake-up speech, in response to determining that the wake-up syllable recognition result represents that the speech to be recognized contains a predetermined syllable (S230).
Claims12
A speech wake-up method, comprising:
- performing a word recognition on a speech to be recognized from a global aspect to obtain a wake-up word recognition result;
- performing a syllable recognition on the speech to be recognized from a local aspect to obtain a wake-up syllable recognition result, in response to determining that the wake-up word recognition result represents that the speech to be recognized contains a predetermined wake-up word;
- and
- determining that the speech to be recognized is a correct wake-up speech, in response to determining that the wake-up syllable recognition result represents that the speech to be recognized contains a predetermined syllable,
- wherein the performing the word recognition on the speech to be recognized from the global aspect to obtain the wake-up word recognition result comprises:
- performing a convolution on the speech to be recognized to obtain a first-level feature vector sequence, wherein the speech to be recognized comprises a speech frame sequence, and the first-level feature vector sequence corresponds to the speech frame sequence;
- performing a gate recurrent operation on the first-level feature vector sequence to obtain a second-level feature vector sequence, and
- performing a classification on the second-level feature vector sequence to obtain the wake-up word recognition result,
- wherein the performing the gate recurrent operation on the first-level feature vector sequence to obtain the second-level feature vector sequence comprises repeatedly performing an operation comprising:
- determining an update gate of a current moment and a candidate hidden layer information of the current moment based on an output vector of a previous moment and an input vector of the current moment, wherein the input vector of the current moment is a first-level feature vector at the current moment in the first-level feature vector sequence;
- determining a hidden layer information of the current moment based on the candidate hidden layer information of the current moment, a hidden layer information of the previous moment, and the update gate of the current moment;
- and
- determining an output vector of the current moment based on the hidden layer information of the current moment and a predetermined parameter, wherein the output vector of the current moment is a second-level feature vector at the current moment in the second-level feature vector sequence,
- wherein the predetermined parameter is a projection parameter, the predetermined parameter is determined based on a threshold of the number of lightweight parameters, the threshold of the number of lightweight parameters refer to a parameter setting benchmark, so that a size of the predetermined parameter is less than or equal to the threshold of the number of lightweight parameters.
11 more elements
An electronic device, comprising:
- at least one processor;
- and
- a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to:
- perform a word recognition on a speech to be recognized from a global aspect to obtain a wake-up word recognition result;
- perform a syllable recognition on the speech to be recognized from a local aspect to obtain a wake-up syllable recognition result, in response to determining that the wake-up word recognition result represents that the speech to be recognized contains a predetermined wake-up word;
- and
- determine that the speech to be recognized is a correct wake-up speech, in response to determining that the wake-up syllable recognition result represents that the speech to be recognized contains a predetermined syllable,
- wherein the at least one processor is further configured to:
- perform a convolution on the speech to be recognized to obtain a first-level feature vector sequence, wherein the speech to be recognized comprises a speech frame sequence, and the first-level feature vector sequence corresponds to the speech frame sequence;
- perform a gate recurrent operation on the first-level feature vector sequence to obtain a second-level feature vector sequence;
- and
- perform a classification on the second-level feature vector sequence to obtain the wake-up word recognition result,
- wherein the at least one processor is further configured to repeatedly perform an operation comprising:
- determining an update gate of a current moment and a candidate hidden layer information of the current moment based on an output vector of a previous moment and an input vector of the current moment, wherein the input vector of the current moment is a first-level feature vector at the current moment in the first-level feature vector sequence;
- determining a hidden layer information of the current moment based on the candidate hidden layer information of the current moment, a hidden layer information of the previous moment, and the update gate of the current moment;
- and
- determining an output vector of the current moment based on the hidden layer information of the current moment and a predetermined parameter, wherein the output vector of the current moment is a second-level feature vector at the current moment in the second-level feature vector sequence,
- wherein the predetermined parameter is a projection parameter, the predetermined parameter is determined based on a threshold of the number of lightweight parameters, the threshold of the number of lightweight parameters refer to a parameter setting benchmark, so that a size of the predetermined parameter is less than or equal to the threshold of the number of lightweight parameters.
15 more elements
A non-transitory computer-readable storage medium having computer instructions therein, wherein the computer instructions are configured to cause a computer to:
- perform a word recognition on a speech to be recognized from a global aspect to obtain a wake-up word recognition result;
- perform a syllable recognition on the speech to be recognized from a local aspect to obtain a wake-up syllable recognition result, in response to determining that the wake-up word recognition result represents that the speech to be recognized contains a predetermined wake-up word;
- and
- determine that the speech to be recognized is a correct wake-up speech, in response to determining that the wake-up syllable recognition result represents that the speech to be recognized contains a predetermined syllable,
- wherein the computer instructions are further configured to cause the computer to:
- perform a convolution on the speech to be recognized to obtain a first-level feature vector sequence, wherein the speech to be recognized comprises a speech frame sequence, and the first-level feature vector sequence corresponds to the speech frame sequence;
- perform a gate recurrent operation on the first-level feature vector sequence to obtain a second-level feature vector sequence;
- and
- perform a classification on the second-level feature vector sequence to obtain the wake-up word recognition result,
- wherein the computer instructions are further configured to cause the computer to repeatedly perform an operation comprising:
- determining an update gate of a current moment and a candidate hidden layer information of the current moment based on an output vector of a previous moment and an input vector of the current moment, wherein the input vector of the current moment is a first-level feature vector at the current moment in the first-level feature vector sequence;
- determining a hidden layer information of the current moment based on the candidate hidden layer information of the current moment, a hidden layer information of the previous moment, and the update gate of the current moment;
- and
- determining an output vector of the current moment based on the hidden layer information of the current moment and a predetermined parameter, wherein the output vector of the current moment is a second-level feature vector at the current moment in the second-level feature vector sequence,
- wherein the predetermined parameter is a projection parameter, the predetermined parameter is determined based on a threshold of the number of lightweight parameters, the threshold of the number of lightweight parameters refer to a parameter setting benchmark, so that a size of the predetermined parameter is less than or equal to the threshold of the number of lightweight parameters.
12 more elements
Dependencies are inferred from the claim text.