Abstract: Voice customization is an application of voice synthesis that involves synthesizing speech having certain voice characteristics, and/or modifying the voice characteristics of human speech. Certain techniques for voice customization may be used in conjunction with compressing speech for storage and/or transmission. For example, speech may be received at a first device and transformed into a latent representation and/or compressed for storage and/or transmission to a second device. The system may use normalizing flows to transform the source audio to a latent representation having a desired variable distribution, and to transform the latent representation back into audio data. A flow model may be conditioned using first speech attributes when transforming the source audio, and an inverse flow model may use second speech attributes when transforming the latent representation back into audio data. The first and/or second speech attributes may be modified to alter voice characteristics of the transmitted speech.

Abstract: Techniques for generating customized synthetic voices personalized to a user, based on user-provided feedback, are described. A system may determine embedding data representing a user-provided description of a desired synthetic voice and profile data associated with the user, and generate synthetic voice embedding data using synthetic voice embedding data corresponding a profile associated with a user determined to be similar to the current user. Based on user-provided feedback with respect to a customized synthetic voice, generated using synthetic voice characteristics corresponding to the synthetic voice embedding data and presented to the user, and the synthetic voice embedding data, the system may generate new synthetic voice embedding data, corresponding to a new customized synthetic voice. The system may be configured to assign the customized synthetic voice to the user, such that a subsequent user may not be presented with the same customized synthetic voice.

Abstract: Voice customization is an application of voice synthesis that involves synthesizing speech having certain voice characteristics, and/or modifying the voice characteristics of human speech. Certain techniques for voice customization may be used in conjunction with compressing speech for storage and/or transmission. For example, speech may be received at a first device and transformed into a latent representation and/or compressed for storage and/or transmission to a second device. The system may use normalizing flows to transform the source audio to a latent representation having a desired variable distribution, and to transform the latent representation back into audio data. A flow model may conditioned using first speech attributes when transforming the source audio, and an inverse flow model may use second speech attributes when transforming the latent representation back into audio data. The first and/or second speech attributes may be modified to alter voice characteristics of the transmitted speech.

Abstract: Techniques for generating customized synthetic voices personalized to a user, based on user-provided feedback, are described. A system may determine embedding data representing a user-provided description of a desired synthetic voice and profile data associated with the user, and generate synthetic voice embedding data using synthetic voice embedding data corresponding a profile associated with a user determined to be similar to the current user. Based on user-provided feedback with respect to a customized synthetic voice, generated using synthetic voice characteristics corresponding to the synthetic voice embedding data and presented to the user, and the synthetic voice embedding data, the system may generate new synthetic voice embedding data, corresponding to a new customized synthetic voice. The system may be configured to assign the customized synthetic voice to the user, such that a subsequent user may not be presented with the same customized synthetic voice.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.