A method for detecting a voice, the method comprising: acquiring a target voice; and inputting the target voice into a pre-trained deep neural network to obtain whether the target voice has a sub-voice in each of a plurality of preset direction intervals, the deep neural network being used to predict whether the voice has a sub-voice in each of the plurality of direction intervals, wherein the target voice is a voice emitted by at least one sound source, each sound source emits one sub-voice in the target voice, and each sound source corresponds to one direction of arrival; wherein the deep neural network comprises a multi-head fully connected network, and an output of the multi-head fully connected network is used to represent whether the voice has the sub-voice in each of the plurality of direction intervals respectively, wherein direction intervals corresponding to any two fully connected networks in the multi-head fully connected network are different; wherein the deep neural network further comprises a feature-extraction network and a convolutional neural network; the inputting the target voice into a pre-trained deep neural network to obtain whether the target voice has a sub-voice in each of a plurality of preset direction intervals, comprises: inputting the target voice into the pre-trained deep neural network, and extracting a voice feature of the target voice based on the feature-extraction network; and processing the voice feature using the convolutional neural network to obtain a voice feature after convolution to be input into the multi-head fully connected network; wherein the deep neural network further comprises a Fourier transform network; the method being characterised in that: the extracting a voice feature of the target voice based on the feature-extraction network, comprises: performing Fourier transform on the target voice using the Fourier transform network to obtain a plural form vector, which is represented by a real part and an imaginary part; normalizing the real part and the imaginary part of the vector using the feature-extraction network to obtain a normalized real part and a normalized imaginary part; and using the normalized real part and the normalized imaginary part as the voice feature of the target voice. acquiring a target voice; and inputting the target voice into a pre-trained deep neural network to obtain whether the target voice has a sub-voice in each of a plurality of preset direction intervals, the deep neural network being used to predict whether the voice has a sub-voice in each of the plurality of direction intervals, wherein the target voice is a voice emitted by at least one sound source, each sound source emits one sub-voice in the target voice, and each sound source corresponds to one direction of arrival; wherein the deep neural network comprises a multi-head fully connected network, and an output of the multi-head fully connected network is used to represent whether the voice has the sub-voice in each of the plurality of direction intervals respectively, wherein direction intervals corresponding to any two fully connected networks in the multi-head fully connected network are different; wherein the deep neural network further comprises a feature-extraction network and a convolutional neural network; the inputting the target voice into a pre-trained deep neural network to obtain whether the target voice has a sub-voice in each of a plurality of preset direction intervals, comprises: inputting the target voice into the pre-trained deep neural network, and extracting a voice feature of the target voice based on the feature-extraction network; and processing the voice feature using the convolutional neural network to obtain a voice feature after convolution to be input into the multi-head fully connected network; inputting the target voice into the pre-trained deep neural network, and extracting a voice feature of the target voice based on the feature-extraction network; and processing the voice feature using the convolutional neural network to obtain a voice feature after convolution to be input into the multi-head fully connected network; wherein the deep neural network further comprises a Fourier transform network; the method being characterised in that: the extracting a voice feature of the target voice based on the feature-extraction network, comprises: performing Fourier transform on the target voice using the Fourier transform network to obtain a plural form vector, which is represented by a real part and an imaginary part; normalizing the real part and the imaginary part of the vector using the feature-extraction network to obtain a normalized real part and a normalized imaginary part; and using the normalized real part and the normalized imaginary part as the voice feature of the target voice. performing Fourier transform on the target voice using the Fourier transform network to obtain a plural form vector, which is represented by a real part and an imaginary part; normalizing the real part and the imaginary part of the vector using the feature-extraction network to obtain a normalized real part and a normalized imaginary part; and using the normalized real part and the normalized imaginary part as the voice feature of the target voice.