1. A method comprising:
extracting, via a processor, dynamic feature vectors from source speech;
applying a first conversion function to a signal including the extracted dynamic feature vectors to produce converted dynamic feature vectors, the first conversion function having been trained using at least dynamic feature data associated with training source speech and training target speech; and
producing converted speech based on an output of applying the first conversion function.
2. A method according to claim 1, further comprising an initial operation of training a conversion model to obtain the first conversion function.
3. A method according to claim 2, wherein training the conversion model comprises:
extracting static and dynamic feature data from both training source data and training target data;
utilizing the static feature data from both the training source data and the training target data to train a second conversion model; and
utilizing the dynamic feature data from both the training source data and the training target data to train the first conversion model.
4. A method according to claim 3, wherein applying the first conversion function further comprises:
applying the second conversion function to static feature vectors extracted from source speech; and
combining an output of the first conversion function and the second conversion function for use in producing the converted speech.
5. A method according to claim 2, wherein training the first conversion model comprises:
extracting static and dynamic feature data from both training source data and training target data;
combining the static and dynamic feature data to form general feature data; and
utilizing the general feature data to train the first conversion model.
6. A method according to claim 1, wherein producing the converted speech further comprises integrating a result of the applying the conversion function to estimate converted static features and combining the result of the applying the conversion function and the estimated converted static features for use in converted speech production.
7. A method according to claim 1, further comprising:
extracting static feature vectors from source speech; and
combining the static feature vectors and the dynamic feature vectors to produce a general feature vector,
wherein applying the first conversion function comprises applying the first conversion function to the general feature vector for use in producing the converted speech.
8. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
a first executable portion for extracting dynamic feature vectors from source speech;
a second executable portion for applying a first conversion function to a signal including the extracted dynamic feature vectors to produce converted dynamic feature vectors, the first conversion function having been trained using at least dynamic feature data associated with training source speech and training target speech; and
a third executable portion for producing converted speech based on an output of applying the first conversion function.
9. A computer program product according to claim 8, further comprising a fourth executable portion for an initial operation of training a conversion model to obtain the first conversion function.
10. A computer program product according to claim 9, wherein the fourth executable portion includes instructions for:
extracting static and dynamic feature data from both training source data and training target data;
utilizing the static feature data from both the training source data and the training target data to train a second conversion model; and
utilizing the dynamic feature data from both the training source data and the training target data to train the first conversion model.
11. A computer program product according to claim 10, wherein the second executable portion includes instructions for:
applying the second conversion function to static feature vectors extracted from source speech; and
combining an output of the first conversion function and the second conversion function for use in producing the converted speech.
12. A computer program product according to claim 9, wherein the fourth executable portion includes instructions for:
extracting static and dynamic feature data from both training source data and training target data;
combining the static and dynamic feature data to form general feature data; and
utilizing the general feature data to train the first conversion model.
13. A computer program product according to claim 8, wherein the third executable portion includes instructions for integrating a result of the applying the conversion function to estimate converted static features and combining the result of the applying the conversion function and the estimated converted static features for use in converted speech production.
14. A computer program product according to claim 8, further comprising:
a fourth executable portion for extracting static feature vectors from source speech; and
a fifth executable portion for combining the static feature vectors and the dynamic feature vectors to produce a general feature vector,
wherein the second executable portion includes instructions for applying the first conversion function to the general feature vector for use in producing the converted speech.
15. An apparatus comprising a processor and memory including computer program code, the processor and the computer program code configured to, with the processor, cause the apparatus at least to:
extract dynamic feature vectors from source speech;
apply a first conversion function to a signal including the extracted dynamic feature vectors to produce converted dynamic feature vectors, the first conversion function having been trained using at least dynamic feature data associated with training source speech and training target speech, and
produce converted speech based on an output of applying the first conversion function.
16. An apparatus according to claim 15, wherein the memory and the computer program code are further configured to, with the processor, cause the apparatus to perform an initial operation of training a conversion model to obtain the first conversion function.
17. An apparatus according to claim 16, wherein the memory and the computer program code are further configured to, with the processor, cause the apparatus to extract static and dynamic feature data from both training source data and training target data; and
utilize the static feature data from both the training source data and the training target data to train a second conversion model, and to utilize the dynamic feature data from both the training source data and the training target data to train the first conversion model.
18. An apparatus according to claim 17, wherein the memory and the computer program code are further configured to, with the processor, cause the apparatus to:
apply the second conversion function to static feature vectors extracted from source speech; and
combine an output of the first conversion function and an output of the second conversion function for use in producing the converted speech.
19. An apparatus according to claim 16, wherein the memory and the computer program code are further configured to, with the processor, cause the apparatus to extract static and dynamic feature data from both training source data and training target data,
combine the static and dynamic feature data to form general feature data; and
utilize the general feature data to train the first conversion model.
20. An apparatus according to claim 15, wherein the memory and the computer program code are further configured to, with the processor, cause the apparatus to integrate a result of applying the conversion function to estimate converted static features and combining the result of the applying the conversion function and the estimated converted static features for use in converted speech production.
21. An apparatus according to claim 15, wherein the memory and the computer program code are further configured to, with the processor, cause the apparatus to extract static feature vectors from source speech, and wherein the transformation element is configured to combine the static feature vectors and the dynamic feature vectors to produce a general feature vector, and to apply the first conversion function to the general feature vector for use in producing the converted speech.
22. An apparatus comprising:
means for extracting dynamic feature vectors from source speech;
means for applying a first conversion function to a signal including the extracted dynamic feature vectors to produce converted dynamic feature vectors, the first conversion function having been trained using at least dynamic feature data associated with training source speech and training target speech; and
means for producing converted speech based on an output of applying the first conversion function.
23. An apparatus according to claim 22, further comprising means for an initial operation of training a conversion model to obtain the first conversion function.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method of operating a wireless communication device having first processing circuitry and second processing circuitry, the method comprising:
transferring first wireless communications to a wireless network using the first processing circuitry;
identifying a reboot condition for the first processing circuitry, and in response, providing a geographic location of the wireless communication device using geographic position equipment of the wireless communication device to second processing circuitry, and displaying a user interface for communications on the wireless communication device using the second processing circuitry;
rebooting the first processing circuitry while the user interface for communications is displayed, wherein the geographic position equipment of the wireless communication device is unavailable during reboot; and
transferring second wireless communications comprising the geographic location of the wireless communication device to the wireless network using the second processing circuitry while rebooting the first processing circuitry, wherein the second processing circuitry is separate from the first processing circuitry.
2. The method of claim 1 wherein transferring the second wireless communications comprises transferring emergency communications including the geographic location of the wireless communication device.
3. The method of claim 1 wherein displaying the user interface for communications comprises displaying an emergency communication button.
4. The method of claim 1 wherein:
identifying the reboot condition for the first processing circuitry comprises identifying a software update for the first processing circuitry; and
rebooting the first processing circuitry comprises implementing the software update for the first processing circuitry.
5. The method of claim 1 wherein:
identifying the reboot condition for the first processing circuitry comprises identifying a software operating system update for the first processing circuitry; and
rebooting the first processing circuitry comprises implementing the software operating system update for the first processing circuitry.
6. The method of claim 1 wherein:
rebooting the first processing circuitry comprises updating a first software operating system for the first processing circuitry;
displaying the user interface and transferring the second wireless communications using the second processing circuitry comprises using a second software operating system that is different from the first software operating system.
7. The method of claim 1 wherein the first processing circuitry comprises a first microprocessor and the second processing circuitry comprises a second microprocessor that is separate from the first microprocessor.
8. The method of claim 1 wherein identifying the reboot condition for the first processing circuitry comprises identifying when the wireless communication device is not in proximity to a human body.
9. The method of claim 1 wherein identifying the reboot condition for the first processing circuitry comprises identifying when the wireless communication device is charging.
10. The method of claim 1 wherein identifying the reboot condition for the first processing circuitry comprises identifying when the wireless communication device is not in motion.
11. A wireless communication device comprising:
a processing system having first processing circuitry and second processing circuitry that is separate from the first processing circuitry;
a graphic display configured to display a user interface for communications;
the processing system configured to transfer first wireless communications to a wireless network using the first processing circuitry and to identify a reboot condition for the first processing circuitry;
the processing system configured to, in response to identifying the reboot condition, provide a geographic location of the wireless communication device using geographic position equipment of the wireless communication device to the second processing circuitry and to direct the graphical display to display the user interface for communications using the second processing circuitry; and
the processing system configured to reboot the first processing circuitry while the user interface for communications is displayed, wherein the geographic position equipment of the wireless communication device is unavailable during reboot; and
the processing system configured to transfer second wireless communications comprising the geographic location of the wireless communication device to the wireless network using the second processing circuitry while rebooting the first processing circuitry.
12. The wireless communication device of claim 11 wherein the processing system is configured to direct the transfer of emergency communications including the geographic location of the wireless communication device using the using the second processing circuitry while rebooting the first processing circuitry.
13. The wireless communication device of claim 11 wherein the user interface for communications comprises an emergency communication button on the graphic display.
14. The wireless communication device of claim 11 wherein the processing system is configured to identify the reboot condition by identifying a software update the first processing circuitry and to implement the software update for the first processing circuitry during the reboot.
15. The wireless communication device of claim 11 wherein the processing system is configured to identify the reboot condition by identifying a software operating system update the first processing circuitry and to implement the software operating system update for the first processing circuitry during the reboot.
16. The wireless communication device of claim 11 wherein the processing system is configured to identify the reboot condition by identifying an update to a first software operating system for the first processing circuitry and wherein the second processing circuitry uses a second software operating system that is different from the first software operating system.
17. The wireless communication device of claim 11 wherein the first processing circuitry comprises a first microprocessor and the second processing circuitry comprises a second microprocessor that is separate from the first microprocessor.
18. The wireless communication device of claim 11 wherein the processing system is configured to identify the reboot condition by identifying when the wireless communication device is not in proximity to a human body.
19. The wireless communication device of claim 11 wherein the processing system is configured to identify the reboot condition by identifying when the wireless communication device is charging.
20. The wireless communication device of claim 11 wherein the processing system is configured to identify the reboot condition by identifying when the wireless communication device is not in motion.