A speech synthesis method, applied to an electronic device and comprising: • acquiring fundamental frequency information and acoustic feature information from an original speech; • generating an impulse train based on the fundamental frequency information, and inputting the impulse train to a harmonic time-varying filter; • inputting the acoustic feature information into a neural network filter estimator to obtain corresponding impulse response information; • generating, by a noise generator, a noise signal; • determining, by the harmonic time-varying filter, harmonic component information by performing filtering processing based on the input impulse train and the impulse response information; • determining, by a noise time-varying filter, noise component information based on the input impulse response information and the noise signal; and • generating a synthesized speech based on the harmonic component information and the noise component information, characterised in that the neural network filter estimator comprises a neural network unit and an inverse discrete-time Fourier transform unit; and said inputting the acoustic feature information into the neural network filter estimator to obtain the corresponding impulse response information comprises: • inputting the acoustic feature information to the neural network unit for analysis to obtain first complex cepstral information corresponding to harmonics and second complex cepstral information corresponding to noise; and • converting, by the inverse discrete-time Fourier transform unit, the first complex cepstral information and the second complex cepstral information into first impulse response information corresponding to harmonics and second impulse response information corresponding to noise, said determining, by the harmonic time-varying filter, the harmonic component information by performing filtering processing based on the input impulse train and the impulse response information comprises: determining, by the harmonic time-varying filter, the harmonic component information by performing filtering processing based on the input impulse train and the first impulse response information; and said determining, by the noise time-varying filter, the noise component information based on the input impulse response information and the noise signal comprises: determining, by the noise time-varying filter, the noise component information based on the input second impulse response information and the noise signal. • acquiring fundamental frequency information and acoustic feature information from an original speech; • generating an impulse train based on the fundamental frequency information, and inputting the impulse train to a harmonic time-varying filter; • inputting the acoustic feature information into a neural network filter estimator to obtain corresponding impulse response information; • generating, by a noise generator, a noise signal; • determining, by the harmonic time-varying filter, harmonic component information by performing filtering processing based on the input impulse train and the impulse response information; • determining, by a noise time-varying filter, noise component information based on the input impulse response information and the noise signal; and • generating a synthesized speech based on the harmonic component information and the noise component information, the neural network filter estimator comprises a neural network unit and an inverse discrete-time Fourier transform unit; and said inputting the acoustic feature information into the neural network filter estimator to obtain the corresponding impulse response information comprises: • inputting the acoustic feature information to the neural network unit for analysis to obtain first complex cepstral information corresponding to harmonics and second complex cepstral information corresponding to noise; and • converting, by the inverse discrete-time Fourier transform unit, the first complex cepstral information and the second complex cepstral information into first impulse response information corresponding to harmonics and second impulse response information corresponding to noise, • inputting the acoustic feature information to the neural network unit for analysis to obtain first complex cepstral information corresponding to harmonics and second complex cepstral information corresponding to noise; and • converting, by the inverse discrete-time Fourier transform unit, the first complex cepstral information and the second complex cepstral information into first impulse response information corresponding to harmonics and second impulse response information corresponding to noise, said determining, by the harmonic time-varying filter, the harmonic component information by performing filtering processing based on the input impulse train and the impulse response information comprises: determining, by the harmonic time-varying filter, the harmonic component information by performing filtering processing based on the input impulse train and the first impulse response information; and said determining, by the noise time-varying filter, the noise component information based on the input impulse response information and the noise signal comprises: determining, by the noise time-varying filter, the noise component information based on the input second impulse response information and the noise signal.