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US 10,573,294 B2Granted
Speech recognition method based on artificial intelligence and terminal
AssigneeBaidu Online Network Technology (Geijing) Co., Ltd.
InventorsMingming Chen, Xiangang Li, Jue Sun
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Abstract
Embodiments of the present disclosure provide a speech recognition method based on artificial intelligence, and a terminal. The method includes obtaining speech data to be recognized; performing a processing on the speech data to be recognized using a trained sub-band energy normalized acoustic model, to determine an normalized energy feature corresponding to each time-frequency unit in the speech data to be recognized; and determining text data corresponding to the speech data to be recognized according to the normalized energy feature corresponding to each time-frequency unit.
Claims16
A speech recognition method based on artificial intelligence, comprising:
- obtaining speech data to be recognized;
- performing a processing on the speech data to be recognized using a trained sub-band energy normalized acoustic model, to determine a normalized energy feature corresponding to each time-frequency unit in the speech data to be recognized;
- and
- determining text data corresponding to the speech data to be recognized according to the normalized energy feature corresponding to each time-frequency unit;
- wherein before performing the processing on the speech data to be recognized using the trained sub-band energy normalized acoustic model, the method further comprises:
- performing a pre-processing on the speech data to be recognized, to determine an energy value of a filter bank corresponding to each time-frequency unit in the speech data to be recognized,
- wherein performing the processing on the speech data to be recognized using the trained sub-band energy normalized acoustic model comprises:
- performing the processing on the energy value of the filter bank corresponding to each time-frequency unit in the speech data to be recognized using the trained sub-band energy normalized acoustic model,
- wherein determining the normalized energy feature corresponding to each time-frequency unit in the speech data to be recognized comprises:
- determining an energy value E(i, j) of a filter bank corresponding to a jth time-frequency unit in an ith frame of speech data and a smoothing parameter sj corresponding to each sub-band;
- obtaining a smoothed energy value M(i−1, j) of a filter bank corresponding to a jth time-frequency unit in an (i−1)th frame of speech data;
- determining a smoothed energy value M(i, j) of the filter bank corresponding to the jth time-frequency unit in the ith frame of speech data according to the energy value E(i, j), the smoothing parameter sj and the smoothed energy value M(i−1, j);
- and
- determining the normalized energy feature corresponding to the jth time-frequency unit in the ith frame of speech data according to the energy value E(i, j) and the smoothed energy value M(i, j).
11 more elements
A terminal, comprising:
- a memory;
- a processor;
- computer programs stored in the memory and executable by the processor;
- wherein, when the processor executes the computer programs, a speech recognition method based on artificial intelligence is performed, wherein the method includes:
- obtaining speech data to be recognized;
- performing a processing on the speech data to be recognized using a trained sub-band energy normalized acoustic model, to determine a normalized energy feature corresponding to each time-frequency unit in the speech data to be recognized;
- and
- determining text data corresponding to the speech data to be recognized according to the normalized energy feature corresponding to each time-frequency unit;
- performing a pre-processing on the speech data to be recognized, to determine an energy value of a filter bank corresponding to each time-frequency unit in the speech data to be recognized,
- wherein performing the processing on the speech data to be recognized using the trained sub-band energy normalized acoustic model comprises:
- performing the processing on the energy value of the filter bank corresponding to each time-frequency unit in the speech data to be recognized using the trained sub-band energy normalized acoustic model;
- wherein determining the normalized energy feature corresponding to each time-frequency unit in the speech data to be recognized comprises:
- determining an energy value E(i, j) of a filter bank corresponding to a jth time-frequency unit in an ith frame of speech data and a smoothing parameter sj corresponding to each sub-band;
- obtaining a smoothed energy value M(i−1, j) of a filter bank corresponding to a jth time-frequency unit in an (i−1)th frame of speech data;
- determining a smoothed energy value M(i, j) of the filter bank corresponding to the jth time-frequency unit in the ith frame of speech data according to the energy value E(i, j), the smoothing parameter sj and the smoothed energy value M(i−1, j);
- and
- determining the normalized energy feature corresponding to the jth time-frequency unit in the ith frame of speech data according to the energy value E(i, j) and the smoothed energy value M(i, j).
14 more elements
A non-transitory computer readable storage medium, configured to store computer programs, wherein, when the computer programs are executed by a processor, a speech recognition method based on artificial intelligence is performed, wherein the method includes:
- obtaining speech data to be recognized;
- performing a processing on the speech data to be recognized using a trained sub-band energy normalized acoustic model, to determine a normalized energy feature corresponding to each time-frequency unit in the speech data to be recognized;
- and
- determining text data corresponding to the speech data to be recognized according to the normalized energy feature corresponding to each time-frequency unit;
- performing a pre-processing on the speech data to be recognized, to determine an energy value of a filter bank corresponding to each time-frequency unit in the speech data to be recognized,
- wherein performing the processing on the speech data to be recognized using the trained sub-band energy normalized acoustic model comprises:
- performing the processing on the energy value of the filter bank corresponding to each time-frequency unit in the speech data to be recognized using the trained sub-band energy normalized acoustic model;
- wherein determining the normalized energy feature corresponding to each time-frequency unit in the speech data to be recognized comprises:
- determining an energy value E(i, j) of a filter bank corresponding to a jth time-frequency unit in an ith frame of speech data and a smoothing parameter sj corresponding to each sub-band;
- obtaining a smoothed energy value M(i−1, j) of a filter bank corresponding to a jth time-frequency unit in an (i−1)th frame of speech data;
- determining a smoothed energy value M(i, j) of the filter bank corresponding to the jth time-frequency unit in the ith frame of speech data according to the energy value E(i, j), the smoothing parameter sj and the smoothed energy value M(i−1, j), and
- determining the normalized energy feature corresponding to the jth time-frequency unit in the ith frame of speech data according to the energy value E(i, j) and the smoothed energy value M(i, j).
9 more elements
Dependencies are inferred from the claim text.