Communication channel quality improvement system using machine conversions
Technology is described for modifying the speed of an output of a modified message that uses machine learning, comprising receiving message data from a sender to be sent to a recipient. Errors in the message data can be corrected using a normalization service to provide a corrected message. The message data can be converted to and output format, such as a second language using a machine learning translation service. Another operation can comprise setting a speed factor for the message data to be output at a defined rate. The message data can then be sent to the recipient to be reproduced for the recipient at the defined rate.
1. A method for modifying the speed of an output of a modified message, that uses machine learning, comprising:
receiving message data from a sender to be sent to a recipient;
correcting errors in the message data using a normalization service to provide a corrected message having corrected message data;
converting the corrected message data to a second language using a machine learning translation service;
using a speed controller, setting a speed factor for the converted, corrected message data to be output at a defined rate; and
sending the converted, corrected message data to the recipient to be reproduced for the recipient at the defined rate.
2. The method as in claim 1 , wherein the output of the converted message data comprises a playback of the converted message data.
3. The method as in claim 2 , wherein the defined rate comprises a speed of the playback of the converted message data, and wherein the speed of the playback of the message data is increased or decreased based on the speed factor.
4. The method as in claim 1 , further comprising setting the speed factor based in part on a personalization profile for the recipient.
5. The method as in claim 1 , further comprising converting the message data to an output format.
6. The method as in claim 5 , wherein the converting the message data to an output format is performed based on a personalization profile for the recipient.
7. The method as in claim 1 , further comprising converting the message data to an output format that includes at least one of: an accent selection, nationality selection, gender selection, or language selection.
8. The method as in claim 1 , further comprising converting the message data to an output format based, at least in part, on at least one of: a recipient education level, a purchasing profile, an age of the recipient, a known location of the recipient, or a technology type being supported.
9. The method as in claim 1 , further comprising receiving a speed at which the sender's message is played back using a value or classification provided the recipient.
10. The method as in claim 1 , wherein the message data is converted to an intermediate format that is text.
11. A method for modifying the speed of an output of a modified message, that uses machine learning, comprising:
receiving message data from a sender to be sent to a recipient;
correcting errors in the message data using a normalization service to provide a corrected message having corrected message data;
converting the corrected message data to an output format;
using a speed controller, setting a speed factor for the converted, corrected message data to be output at a defined rate; and
sending the converted, corrected message data to the recipient to be reproduced for the recipient at the defined rate.
12. The method as in claim 11 , wherein the output of the converted message data comprises a playback of the converted message data.
13. The method as in claim 12 , wherein the defined rate comprises a speed of the playback of the converted message data, and wherein the speed of the playback of the message data is increased or decreased based on the speed factor.
14. The method as in claim 11 , further comprising setting the speed factor based in part on a personalization profile for the recipient.
15. The method as in claim 11 , wherein the converting the message data to an output format comprises converting the message data to a second language using a machine learning translation service.
16. The method as in claim 11 , wherein the converting the message data to an output format is performed based on a personalization profile for the recipient.
17. The method as in claim 11 , wherein the converting the message data to an output format includes at least one of: an accent selection, nationality selection, gender selection, or language selection.
18. The method as in claim 11 , wherein the converting the message data to an output format is based, at least in part, on at least one of: a recipient education level, a purchasing profile, an age of the recipient, a known location of the recipient, or a technology type being supported.
19. The method as in claim 11 , further comprising receiving a speed at which the sender's converted message data is played back using a value or classification provided the recipient.
20. The method as in claim 11 , further comprising converting the message data to an intermediate format that is text prior to converting it to the output format.