A speech processing method, performed by an electronic device, characterized by the method comprising: obtaining to-be-processed speech information; determining a first speech feature and a first text bottleneck feature based on the to-be-processed speech information, wherein the determining the first text bottleneck feature based on the to-be-processed speech information comprises: extracting an N-dimensional filter-bank feature and an M-dimensional pitch feature from the to-be-processed speech information, wherein N and M are positive integers; splicing the N-dimensional filter-bank feature and the M-dimensional pitch feature to obtain a second speech feature; inputting the second speech feature into a trained automatic speech recognition, ASR, network; and extracting the first text bottleneck feature from a linear layer of a bottleneck of the trained ASR network, wherein the ASR network comprises a deep neural network, DNN, with four hidden layers as an input layer, a linear layer of a bottleneck, and a probability distribution softmax layer as an output layer, wherein an output of a first hidden layer is inputted into a second hidden layer, an output of the second hidden layer is inputted into a third hidden layer, an output of the third hidden layer is inputted into the linear layer of the bottleneck, an output of the linear layer of the bottleneck is inputted into a fourth hidden layer, and an output of the fourth hidden layer is inputted into the softmax layer; determining a first combined feature vector based on the first speech feature and the first text bottleneck feature; and inputting the first combined feature vector to a trained unidirectional long short-term memory, LSTM, model; performing speech processing on the first combined feature vector to obtain speech information after noise reduction; and transmitting the obtained speech information after noise reduction to another electronic device for playing. obtaining to-be-processed speech information; determining a first speech feature and a first text bottleneck feature based on the to-be-processed speech information, wherein the determining the first text bottleneck feature based on the to-be-processed speech information comprises: extracting an N-dimensional filter-bank feature and an M-dimensional pitch feature from the to-be-processed speech information, wherein N and M are positive integers; splicing the N-dimensional filter-bank feature and the M-dimensional pitch feature to obtain a second speech feature; inputting the second speech feature into a trained automatic speech recognition, ASR, network; and extracting the first text bottleneck feature from a linear layer of a bottleneck of the trained ASR network, wherein the ASR network comprises a deep neural network, DNN, with four hidden layers as an input layer, a linear layer of a bottleneck, and a probability distribution softmax layer as an output layer, wherein an output of a first hidden layer is inputted into a second hidden layer, an output of the second hidden layer is inputted into a third hidden layer, an output of the third hidden layer is inputted into the linear layer of the bottleneck, an output of the linear layer of the bottleneck is inputted into a fourth hidden layer, and an output of the fourth hidden layer is inputted into the softmax layer; wherein the determining the first text bottleneck feature based on the to-be-processed speech information comprises: extracting an N-dimensional filter-bank feature and an M-dimensional pitch feature from the to-be-processed speech information, wherein N and M are positive integers; splicing the N-dimensional filter-bank feature and the M-dimensional pitch feature to obtain a second speech feature; inputting the second speech feature into a trained automatic speech recognition, ASR, network; and extracting the first text bottleneck feature from a linear layer of a bottleneck of the trained ASR network, extracting an N-dimensional filter-bank feature and an M-dimensional pitch feature from the to-be-processed speech information, wherein N and M are positive integers; splicing the N-dimensional filter-bank feature and the M-dimensional pitch feature to obtain a second speech feature; inputting the second speech feature into a trained automatic speech recognition, ASR, network; and extracting the first text bottleneck feature from a linear layer of a bottleneck of the trained ASR network, wherein the ASR network comprises a deep neural network, DNN, with four hidden layers as an input layer, a linear layer of a bottleneck, and a probability distribution softmax layer as an output layer, wherein an output of a first hidden layer is inputted into a second hidden layer, an output of the second hidden layer is inputted into a third hidden layer, an output of the third hidden layer is inputted into the linear layer of the bottleneck, an output of the linear layer of the bottleneck is inputted into a fourth hidden layer, and an output of the fourth hidden layer is inputted into the softmax layer; determining a first combined feature vector based on the first speech feature and the first text bottleneck feature; and inputting the first combined feature vector to a trained unidirectional long short-term memory, LSTM, model; performing speech processing on the first combined feature vector to obtain speech information after noise reduction; and transmitting the obtained speech information after noise reduction to another electronic device for playing.