A method for generating speech through multilingual neural text-to-speech, TTS, synthesis, comprising: receiving a text input in at least a first language; providing, through a speaker encoder, speaker latent space information of a target speaker; providing, through a language encoder, language latent space information of a second language; generating, through an acoustic feature predictor, at least one acoustic feature based on the text input, the speaker latent space information and the language latent space information of the second language; and generating, through a neural vocoder (120, 220, a speech waveform corresponding to the text input based on the at least one acoustic feature; wherein language latent space information comprises a representation of prosody of a language in a multi-dimension space which reflects one or more characteristics of the language in a prosody level comprising at least one of a tone, a rhythm and a speech speed; wherein the speaker latent space information corresponds to a corpus of the target speaker in a second language different from the first language and is a representation of voice characteristics of a speaker in a multi-dimension space; wherein the acoustic feature contains target speaker characteristics and second language characteristics; providing, through the language encoder, language latent space information of a third language; and interpolating the language latent space information of the second language and the language latent space information of the third language, to obtain interpolated language latent space information, wherein generating at least one acoustic feature comprises: generating the at least one acoustic feature based on the text input, the speaker latent space information and the interpolated language latent space information. receiving a text input in at least a first language; providing, through a speaker encoder, speaker latent space information of a target speaker; providing, through a language encoder, language latent space information of a second language; generating, through an acoustic feature predictor, at least one acoustic feature based on the text input, the speaker latent space information and the language latent space information of the second language; and generating, through a neural vocoder (120, 220, a speech waveform corresponding to the text input based on the at least one acoustic feature; wherein language latent space information comprises a representation of prosody of a language in a multi-dimension space which reflects one or more characteristics of the language in a prosody level comprising at least one of a tone, a rhythm and a speech speed; wherein the speaker latent space information corresponds to a corpus of the target speaker in a second language different from the first language and is a representation of voice characteristics of a speaker in a multi-dimension space; wherein the acoustic feature contains target speaker characteristics and second language characteristics; providing, through the language encoder, language latent space information of a third language; and interpolating the language latent space information of the second language and the language latent space information of the third language, to obtain interpolated language latent space information, wherein generating at least one acoustic feature comprises: generating the at least one acoustic feature based on the text input, the speaker latent space information and the interpolated language latent space information.