Abstract: Methods and apparatuses are provided for facilitating speech synthesis. A method may include generating a plurality of input models representing an input by using a statistical model synthesizer to statistically model the input. The method may further include determining a speech unit sequence representing at least a portion of the input by using the input models to influence selection of one or more pre-recorded speech units having parameter representations. The method may additionally include identifying one or more bad units in the unit sequence. The method may also include replacing the identified one or more bad units with one or more parameters generated by the statistical model synthesizer. Corresponding apparatuses are also provided.

Abstract: Methods and apparatuses are provided for facilitating speech synthesis. A method may include generating a plurality of input models representing an input by using a statistical model synthesizer to statistically model the input. The method may further include determining a speech unit sequence representing at least a portion of the input by using the input models to influence selection of one or more pre-recorded speech units having parameter representations. The method may additionally include identifying one or more bad units in the unit sequence. The method may also include replacing the identified one or more bad units with one or more parameters generated by the statistical model synthesizer. Corresponding apparatuses are also provided.

Abstract: An apparatus for providing improved voice conversion includes a sub-feature generator and a transformation element. The sub-feature generator may be configured to define sub-feature units with respect to a feature of source speech. The transformation element may be configured to perform voice conversion of the source speech to target speech based on the conversion of the sub-feature units to corresponding target speech sub-feature units using a conversion model trained with respect to converting training source speech sub-feature units to training target speech sub-feature units.

Abstract: A contour for a syllable (or other speech segment) in a voice undergoing conversion is transformed. The transform of that contour is then used to identify one or more source syllable transforms in a codebook. Information regarding the context and/or linguistic features of the contour being converted can also be compared to similar information in the codebook when identifying an appropriate source transform. Once a codebook source transform is selected, an inverse transformation is performed on a corresponding codebook target transform to yield an output contour. The corresponding codebook target transform represents a target voice version of the same syllable represented by the selected codebook source transform. The output contour may be further processed to improve conversion quality.

Abstract: It may be desirable to provide a way to collect high quality speech training data without undue burden to the user. Speech training data may be collected during normal usage of a device. In this way, the collection of speech training data may be effectively transparent to the user, without the need for a distinct collection mode from the user's point of view. For example, where the device is or includes a phone (such as a cellular phone), when the user makes or receives a phone call to/from another party, speech training data may be automatically collected from one or both of the parties during the phone call.

Abstract: An apparatus for providing improved voice conversion includes a sub-feature generator and a transformation element. The sub-feature generator may be configured to define sub-feature units with respect to a feature of source speech. The transformation element may be configured to perform voice conversion of the source speech to target speech based on the conversion of the sub-feature units to corresponding target speech sub-feature units using a conversion model trained with respect to converting training source speech sub-feature units to training target speech sub-feature units.

Abstract: It may be desirable to provide a way to collect high quality speech training data without undue burden to the user. Speech training data may be collected during normal usage of a device. In this way, the collection of speech training data may be effectively transparent to the user, without the need for a distinct collection mode from the user's point of view. For example, where the device is or includes a phone (such as a cellular phone), when the user makes or receives a phone call to/from another party, speech training data may be automatically collected from one or both of the parties during the phone call.

Abstract: A contour for a syllable (or other speech segment) in a voice undergoing conversion is transformed. The transform of that contour is then used to identify one or more source syllable transforms in a codebook. Information regarding the context and/or linguistic features of the contour being converted can also be compared to similar information in the codebook when identifying an appropriate source transform. Once a codebook source transform is selected, an inverse transformation is performed on a corresponding codebook target transform to yield an output contour. The corresponding codebook target transform represents a target voice version of the same syllable represented by the selected codebook source transform. The output contour may be further processed to improve conversion quality.