The invention relates to a cluster voice transforming method and system based on a mixture Gaussian random process. The cluster voice transforming method comprises the steps that (S100) a training section is conducted, in other words, characteristic parameter extraction and training are conducted on source voice data and target voice data; (S200) a transformation section is conducted, in other words, the source characteristic parameters extracted again and training results are transformed and combined to obtain reconstructed voice data. The cluster voice transformation scheme is suitable for large data volume environments where the data are high in relevance and superposition; for the case of the Chinese voice, the voice metadata essentially forming the voice are limit under the voice with rich superficial changes; thus, by means of establishment of the voice transformation method with the mixture structure, cluster modeling can be conducted on the voice data, so that big data are fully utilized, and the system performance is improved.

Cluster voice transforming method and system based on mixture Gaussian random process

The invention discloses a sound track spectrum Gaussian mixture model based rapid voice conversion system and method. The method comprises the steps of parameter extraction and synthesis, characteristic parameter time aligning and characteristic parameter training and conversion. By the technologies of fixation of the Gaussian average on Mel frequency spectra, adaptive Gaussian variance adjusting, selecting of sampling points as weight coefficients on logarithm magnitude spectra and the like, the calculation complexity of voice parameter characterization is greatly reduced, and the operating rate is improved greatly.

Sound track spectrum Gaussian mixture model based rapid voice conversion system and method

The present invention provides a voice noise reduction method and device, a server and a storage medium. The method comprises the steps of: obtaining voice signals synchronously collected by an acoustic microphone and a non-acoustic microphone; performing voice activity detection according to the voice signals collected by the non-acoustic microphone, and obtaining a voice activity detection result; and performing noise reduction of the voice signals collected by the acoustic microphone according to the voice activity detection result, and obtaining the voice signals after noise reduction. Theeffect of noise reduction is enhanced, and the quality of the voice signals is improved.

Voice noise reduction method and device, server and storage medium

The invention relates to a method and a device used for computerized matching based on an emotion framework The method used for computerized matching based on the emotion framework provided by one embodiment of the invention comprises the following steps: receiving at least one audio signal coming from a user; extracting features from the at least one audio signal, so that a first feature matrixwith a first dimensionality is generated; converting the first feature matrix into a second feature matrix with a second dimensionality, wherein the second dimensionality is smaller than the first dimensionality; feeding the second feature matrix and the data of a potential matched object to a matching model; and generating a matching result for the user through the matching model For the embodiment of the invention, features can be accurately and effectively extracted from voice of the user, the dimensionality of the features is reduced, and thus more successful matching is obtained based onthe emotion framework but not the use mode

Method and device used for computerized matching based on emotion framework

The invention provides a voice converting method. The method includes the following steps of S1, extracting voice features of a voice source and target voice in a voice file; S2, conducting dynamic time scheduling on the voice information of the voice source and the target voice; S3, training scheduled voice by means of a Gaussian hybrid model and a cluster algorithm; S4, extracting the voice information of the voice source and converting the voice information through the trained data to synthesize the target voice. The precise and efficient method for converting the voice of the voice sourceinto the voice of the target voice is provided, and the voice of a source speaker can be accurately converted into the voice of a target speaker by conducting modeling and algorithm calculating on thevoice of the source speaker and the target speaker according to the mathematic characteristics of the source speaker and the target speaker.

Voice converting method

The invention discloses a voice conversion system based on a hidden Gaussian random field. The voice conversion system comprises a voice analysis module, a voice synthesis module, a voice parameter pre-processing module and a voice parameter conversion mapping module. The voice analysis module and the voice synthesis module are used for carrying out decomposition and reconstruction on original voice signals. The voice parameter pre-processing module is used for sorting and screening feature parameters of a speaker A and a speaker B to obtain feature parameter sets synchronous in time. The voice parameter conversion mapping module is used for capturing the mapping relation between the feature parameter set A and the feature parameter set B to obtain a mapping rule. The core technology of the system expands around the Gaussian random field theory, a novel hidden Gaussian random field model is generated by changing the structure of the basic Gaussian random field, and the system can achieve an ideal effect under the environment of a lack of data.

Voice conversion system based on hidden Gaussian random field

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Voice conversion system based on hidden Gaussian random field