35 results about "Speech reconstruction" patented technology

A method, system and computer program product for voice conversion. The method includes performing speech analysis on the speech of a source speaker to achieve speech information; performing spectral conversion based on said speech information, to at least achieve a first spectrum similar to the speech of a target speaker; performing unit selection on the speech of said target speaker at least using said first spectrum as a target; replacing at least part of said first spectrum with the spectrum of the selected target speaker's speech unit; and performing speech reconstruction at least based on the replaced spectrum.

A speech signal processing system is described for use with automatic speech recognition and hands free speech communication. A signal pre-processor module transforms an input microphone signal into corresponding speech component signals. A noise suppression module applies noise reduction to the speech component signals to generate noise reduced speech component signals. A speech reconstruction module produces corresponding synthesized speech component signals for distorted speech component signals. A signal combination block adaptively combines the noise reduced speech component signals and the synthesized speech component signals based on signal to noise conditions to generate enhanced speech component signals for automatic speech recognition and hands free speech communication.

The invention discloses a speech enhancement system and method based on a multi-subband short-time fractional Fourier spectrum random rearrangement robust principal component analysis MFrSRRPCA algorithm. The realization steps are: a time-frequency analysis module generates time-frequency information of noisy speech; the time-frequency analysis module generates time-frequency information of noisyspeech. The time-frequency subband division module divides the time-frequency amplitude spectrum of the noisy speech into a plurality of noisy subbands. Each time-frequency amplitude spectrum enhancement module randomly disrupts the sequence of each frame spectrum element in the corresponding noisy sub-band, and generates the corresponding enhancement sub-band by using a robust principal componentanalysis algorithm according to the noise intensity estimation value in the corresponding sub-band. The time-frequency subband recombination module composes all the enhancement subbands to enhance the time-frequency amplitude spectrum. The time-domain speech reconstruction module reconstructs the enhanced time-frequency amplitude spectrum into enhanced speech. The invention can improve the soundquality and intelligibility of the noisy speech, and can be used for the speech enhancement and noise reduction of the speech receiving system.

The invention relates to a cross-modal generation method based on voice and a face image. The method comprises the steps of voice reconstruction of a face and personalized voice synthesis of the faceimage. A voice reconstruction face model based on residual priori is provided for voice reconstruction of a face, and the face of the person is generated according to an input section of unknown voice. According to personalized voice synthesis of the face image, a face image personalized voice synthesis model based on residual priori is provided, and the voice of the person is synthesized according to the given face image and a section of text. The invention is scientific and reasonable in design, the effect of the voice reconstruction face model can generate the face image very similar to theoriginal face, the robustness is very high, the number of the generated faces is not a fixed number, the voice of any speaker is input, and the face similar to the speaker can be reconstructed. And the residual priori face image personalized speech synthesis model is also used for synthesizing the speech of the person according to any face image. In addition, the proposed residual priori knowledge method can accelerate convergence of the model and achieve a better effect.

The invention provides a video synthesis model training method and device, a video synthesis method and device, a storage medium, a program product and electronic equipment, and the method comprises the steps: obtaining a sample text and a sample video which is a video that a real person reads the sample text; inputting the sample text into the speech synthesis sub-model to obtain a feature vector; inputting the feature vectors into a voice reconstruction human face sub-model to obtain human face feature parameters; inputting the face feature parameters and the sample video into the differentiable rendering sub-model to obtain a face feature map; and inputting the face feature map into the generative adversarial network sub-model to obtain a virtual real person video, and iteratively training the voice reconstruction face sub-model, the differentiable rendering sub-model and the generative adversarial network sub-model based on the virtual real person video and the sample video until a loss function value of the generative adversarial network sub-model meets a preset condition.

The invention provides a colored repeated sentence spectrum construction method for speech reconstruction, and belongs to the technical field of speech signal processing. The method is characterized in that two color channels respectively serve as a real part and an imaginary part in fourier transform; the position coordinates of R-B synthetic color in an R-G-B color space are corresponding to the real part and the imaginary part in the fourier transform, wherein G is the symbolic combination of the real part and the imaginary part. According to the method, the real part, the imaginary and the symbols of the real part and the imaginary, corresponding to the repeating number, can be analyzed according to the R-G-B color ratio; the speech spectrum is subjected to image processing, then the speech is reconstructed, thus the fourier transform can be performed by enhancing the speech by the image processing technology and the like, and as a result, the speech reconstruction is realized.

The invention discloses a soundless voice reconstruction method based on a sound production neural potential signal, and the method comprises the steps: collecting a sound production related neural potential signal and a voice signal during sound production through an electrode, and collecting the sound production related neural signal under a soundless condition; moreover, the connection between the silent neural potential signal and the voice signal of the same text is obtained through a deep learning network, the reconstruction of the voice signal under the silent condition, the transmission of the information content under the silent condition and the task of silent information transmission are completed, and the information transmission under the silent condition can be directly carried out. For some occasions where sound cannot be produced, the vocal cord does not vibrate, and the information transmission process is completed in a tacit reading mode.

The invention discloses a voice processing system and method and an intelligent fume hood system based on active noise reduction. The system comprises a voice collection module, an exhaust fan parameter obtaining module, a noise reduction module and a voice reconstruction module, wherein the voice collection module is used for collecting voice and converting the voice into a digital signal; the exhaust fan parameter obtaining module is used for obtaining the rotating speed of an exhaust fan; the noise reduction module is used for obtaining noise signals of the exhaust fan and converting the noise signals into noise reduction signals, the noise reduction module is used for receiving output data of the exhaust fan parameter obtaining module, fundamental frequency and sound pressure are obtained through calculation in combination with the number of blades of the exhaust fan, the diameter of an impeller and power, and therefore the noise signals are obtained; and the voice reconstruction module is used for superposing the noise reduction signals and output signals of the voice collection module to obtain a reconstruction signal. According to the system, the noise signal of the exhaustfan is obtained by directly measuring the rotating speed of the exhaust fan and combining the number of the blades of the exhaust fan, the noise analysis is accurate, and the noise reduction accuracyand the noise reduction effect in the exhaust environment using the exhaust fan are improved.