Speech recognition method based on artificial intelligence and terminal
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.
1. 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 s j 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 s j 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).
2. The method according to claim 1 , before performing the processing on the speech data to be recognized using a preset sub-band energy normalized acoustic model, further comprising:
obtaining training speech data;
training an initial sub-band energy normalized acoustic model using the training speech data, to determine the trained sub-band energy normalized acoustic model and a smoothing parameter corresponding to each sub-band.
3. The method according to claim 2 , before training the initial sub-band energy normalized acoustic model using the training speech data, further comprising:
determining an initial smoothing parameter corresponding to each sub-band according to a preset rule.
4. The method according to claim 1 , wherein the pre-processing comprises at least one of a pre-emphasis processing, a framing, a Han windowing, a quick Fourier transform processing, a quadratic energy processing, a Mel filtering, a processing of taking the logarithm.
5. The method according to claim 1 , wherein obtaining the 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 a formula: M(i,j)=(1−s j )M(i−1, j)+s j E(i,j).
6. The method according to claim 1 , wherein determining the normalized energy feature corresponding to the jth time-frequency unit in the ith frame of speech data according to a formula:
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where ò is a preset minimum value,
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represents a feed forward automatic gain control, of which a strength is controlled by α, and σ and γ are square root compression parameters.
7. The method according to claim 1 , wherein determining text data corresponding to the speech data to be recognized comprises:
inputting the normalized energy feature corresponding to each time-frequency unit into a neural network model; and
determining the text data corresponding to the speech data to be recognized by the neural network model.
8. The method according to claim 7 , further comprising:
generating the neural network model.
9. The method according to claim 8 , wherein generating the neural network model comprises:
obtaining a large amount of training speech data and corresponding training text data;
determining normalized energy feature corresponding to each time-frequency unit in the large amount of speech data;
performing a training according to the normalized energy feature corresponding to each time-frequency unit in the large amount of speech data and corresponding training text data, to generate the neural network model.
10. 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 s j 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 s j 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. The terminal according to claim 10 , wherein the method further comprises:
obtaining training speech data;
training an initial sub-band energy normalized acoustic model using the training speech data, to determine the trained sub-band energy normalized acoustic model and a smoothing parameter corresponding to each sub-band.
12. The terminal according to claim 11 , wherein the method further comprises:
determining an initial smoothing parameter corresponding to each sub-band according to a preset rule.
13. The terminal according to claim 10 , wherein the pre-processing comprises at least one of a pre-emphasis processing, a framing, a Han windowing, a quick Fourier transform processing, a quadratic energy processing, a Mel filtering, a processing of taking the logarithm.
14. The terminal according to claim 10 , wherein obtaining the 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 a formula: M(i,j)=(1−s j )M(i−1,j)+s j E(i,j).
15. The terminal according to claim 10 , wherein determining the normalized energy feature corresponding to the jth time-frequency unit in the ith frame of speech data according to a formula:
PCEN
(
i
,
j
)
=
(
E
(
i
,
j
)
(
o
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+
M
(
i
,
j
)
)
α
+
σ
)
γ
-
σ
γ
,
where ò is a preset minimum value,
E
(
i
,
j
)
(
o
‵
+
M
(
i
,
j
)
)
α
represents a feed forward automatic gain control, of which a strength is controlled by α, and σ and γ are square root compression parameters.
16. 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 s j 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 s j 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).