Speech synthesis method and system

1. 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; and

generating a synthesized speech based on the harmonic component information and the noise component information,

wherein 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.

2. The method according to claim 1 , wherein,

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 comprises: determining, by the noise time-varying filter, the noise component information based on the input second impulse response information and the noise.

3. The method according to claim 1 , wherein said generating the synthesized speech based on the harmonic component information and the noise component information comprises:

inputting the harmonic component information and the noise component information to a finite-length mono-impulse response system to generate the synthesized speech.

4. A speech synthesis system, applied to an electronic device and comprising:

an impulse train generator configured to generate an impulse train based on fundamental frequency information of an original speech;

a neural network filter estimator configured to obtain corresponding impulse response information by taking acoustic feature information of the original speech as input;

a random noise generator configured to generate a noise signal;

a harmonic time-varying filter configured to determine harmonic component information by performing filtering processing based on the input impulse train and the impulse response information;

a noise time-varying filter configured to determine noise component information based on the input impulse response information and the noise; and

an impulse response system configured to generate a synthesized speech based on the harmonic component information and the noise component information,

wherein the neural network filter estimator comprises a neural network unit and an inverse discrete-time Fourier transform unit; and

said obtaining the corresponding impulse response information by taking the acoustic feature information of the original speech as input 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.

5. The system according to claim 4 , wherein,

said determining 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 the noise component information based on the input impulse response information and the noise comprises: determining, by the noise time-varying filter, the noise component information based on the input second impulse response information and the noise.

6. The system according to claim 4 , wherein the speech synthesis system adopts the following optimized training method before being used for speech synthesis:

the speech synthesis system is trained using a multi-resolution STFT loss and an adversarial loss for the original speech and the synthesized speech.

7. An electronic device comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the method of claim 1 .

8. A non-transitory storage medium on which a computer program is stored, wherein the program, when being executed by a processor, performs the steps of the method of claim 1 .

9. The system according to claim 4 , wherein the speech synthesis system adopts the following optimized training method before being used for speech synthesis:

the speech synthesis system is trained using a multi-resolution STFT loss and an adversarial loss for the original speech and the synthesized speech.

10. The system according to claim 5 , wherein the speech synthesis system adopts the following optimized training method before being used for speech synthesis:

the speech synthesis system is trained using a multi-resolution STFT loss and an adversarial loss for the original speech and the synthesized speech.