Speech recognition and codec method and apparatus, electronic device and storage medium

A computer-implemented method for encoding and decoding, comprising:

• acquiring a feature corresponding to a to-be-processed object, the feature comprising at least a feature obtained according to historical output information of a decoder, wherein the output information comprises:
• hidden layer features outputted by the decoder, the acquiring a feature corresponding to a to-be-processed object comprises:
• acquiring historical feature abstraction information corresponding to the to-be-processed object, the historical feature abstraction information being information obtained by feature abstraction of historical processing objects;
• encoding the feature by using an encoder, to obtain an encoding result;
• and
• decoding the encoding result by using the decoder, to obtain a processing result corresponding to the to-be-processed object;
• performing feature abstraction on the to-be-processed object by using the hidden layer features acquired from the decoder, to obtain a feature vector with a predetermined length, and taking the feature vector as a historical feature vector corresponding to the to-be-processed object;
• wherein the acquiring historical feature abstraction information corresponding to the to-be-processed object comprises:
• splicing historical feature vectors corresponding to the historical processing objects respectively, and taking a splicing result as the historical feature abstraction information.

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected with the at least one processor;
• wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a speech recognition, wherein the speech recognition method comprises:
• acquiring an audio feature of to-be-recognized speech collected by a user equipment in real time;
• encoding the audio feature to obtain an encoding feature;
• truncating the encoding feature to obtain continuous N feature fragments with unequal lengths, N being a positive integer greater than one;
• acquiring, for any one of the feature segments, corresponding historical feature abstraction information, encoding the feature segment in combination with the historical feature abstraction information, and decoding an encoding result to obtain a recognition result corresponding to the feature segment, wherein the historical feature abstraction information is information obtained by feature abstraction of recognized historical feature fragments, wherein the encoding the feature segment in combination with the historical feature abstraction information comprises:
• encoding the feature segment in combination with the historical feature abstraction information by using a second encoder;
• and the decoding an encoding result comprises:
• decoding the encoding result by using a first decoder;
• wherein the historical feature abstraction information is generated according to output information of the decoder, wherein the second encoder comprises a P-layer convolution-augmented Conformer model, P being a positive integer;
• a number of the first decoder is Q, Q is a positive integer, and the Q first decoders are successively connected;
• and
• performing the following processing for any Conformer model:
• performing feature abstraction on a feature fragment currently processed in the Conformer model by using hidden layer features acquired from any first decoder, to obtain a feature vector with a predetermined length, and taking the feature vector as a historical feature vector of the currently processed feature fragment corresponding to the Conformer model, wherein the acquiring corresponding historical feature abstraction information comprises:
• for any Conformer model, splicing historical feature vectors of historical feature fragments corresponding to the Conformer model respectively, and taking a splicing result as the historical feature abstraction information.

A non-transitory computer readable storage medium with computer instructions stored thereon, wherein the computer instructions are used for causing a speech recognition method, wherein the speech recognition method comprises:

• acquiring an audio feature of to-be-recognized speech;
• encoding the audio feature to obtain an encoding feature;
• truncating the encoding feature to obtain continuous N feature fragments with unequal lengths, N being a positive integer greater than one;
• acquiring, for any one of the feature segments, corresponding historical feature abstraction information, encoding the feature segment in combination with the historical feature abstraction information, and decoding an encoding result to obtain a recognition result corresponding to the feature segment, wherein the historical feature abstraction information is information obtained by feature abstraction of recognized historical feature fragments, wherein the encoding the feature segment in combination with the historical feature abstraction information comprises:
• encoding the feature segment in combination with the historical feature abstraction information by using a second encoder;
• and the decoding an encoding result comprises:
• decoding the encoding result by using a first decoder;
• wherein the historical feature abstraction information is generated according to output information of the decoder, wherein the second encoder comprises a P-layer convolution-augmented Conformer model, P being a positive integer;
• a number of the first decoder is Q, Q is a positive integer, and the Q first decoders are successively connected;
• and
• performing the following processing for any Conformer model:
• performing feature abstraction on a feature fragment currently processed in the Conformer model by using hidden layer features acquired from any first decoder, to obtain a feature vector with a predetermined length, and taking the feature vector as a historical feature vector of the currently processed feature fragment corresponding to the Conformer model, wherein the acquiring corresponding historical feature abstraction information comprises:
• for any Conformer model, splicing historical feature vectors of historical feature fragments corresponding to the Conformer model respectively, and taking a splicing result as the historical feature abstraction information.