Methods and systems for voice recognition in autonomous flight of an electric aircraft

1. A system for voice recognition in autonomous flight of an electric aircraft, the system comprising:

a computing device communicatively connected to the electric aircraft, the computing device is configured to:

receive at least a voice datum from a remote device, wherein the voice datum is configured to include at least an expression datum;

generate, using a first machine-learning process, a transcription datum as a function of the at least a voice datum;

extract at least a query as a function of the transcription datum;

generate, using a second machine-learning process, a communication output as a function of the at least a query; and

adjust a flight plan as a function of the communication output.

2. The system of claim 1 , wherein the computing device is further configured to transmit an adjustment notification to an interface device as a function of adjusting the flight plan.

3. The system of claim 2 , wherein the system is further configured to include an interface device, wherein the interface device is configured to display the adjustment notification to a user.

4. The system of claim 1 , wherein the computing device is further configured to transmit the transcription datum to an interface device.

5. The system of claim 1 , wherein generating the transcription datum further comprises:

selecting a correlated dataset containing a plurality of data entries wherein each dataset contains at least a datum of expression data and at least a first correlated content datum as a function of the at least a voice datum; and

generating, at a clustering unsupervised machine-learning model, a transcription datum as a function of the at least a voice datum and the correlated dataset.

6. The system of claim 1 , wherein the at least a query is extracted at a language processing module.

7. The system of claim 1 , wherein generating the communication output further comprises:

receiving, at a supervised machine-learning model, a first training set as a function of the at least a voice datum and the at least a query; and

generating, at a supervised machine-learning model, a communication output as a function of relating the at least a query to at least a textual output.

8. The system of claim 1 , wherein the computing device is further configured to:

generate a response datum as a function of the communication output; and

transmit the response datum to the remote device.

9. The system of claim 1 , wherein receiving the voice datum further comprises storing the voice datum in the datastore system.

10. A method for voice recognition in autonomous flight of an electric aircraft, the method comprising:

receiving, at a computing device, at least a voice datum from a remote device, wherein the voice datum is configured to include at least an expression datum;

generating, at the computing device, using a first machine-learning process, a transcription datum as a function of the at least a voice datum;

extracting, at the computing device, at least a query as a function of the transcription datum;

generating, at the computing device, using a second machine-learning process, a communication output as a function of the at least a query; and

adjusting, at the computing device, a flight plan as a function of the communication output.

11. The method of claim 10 , wherein the method further comprises transmitting, by the computing device, an adjustment notification to a user interface device as a function of adjusting the flight plan.

12. The method of claim 11 , wherein the method further comprises displaying, by the user interface device, the adjustment notification to a user.

13. The method of claim 10 , wherein the method further comprises transmitting, by the computing device, the transcription datum to a user interface device.

14. The method of claim 10 , wherein generating the transcription datum further comprises:

selecting a correlated dataset containing a plurality of data entries wherein each dataset contains at least a datum of expression data and at least a first correlated content datum as a function of the at least a voice datum; and

generating, at a clustering unsupervised machine-learning model, a transcription datum as a function of the at least a voice datum and the correlated dataset.

15. The method of claim 10 , wherein the at least a query is extracted at a language processing module.

16. The method of claim 10 , wherein generating the communication output further comprises:

receiving, at a supervised machine-learning model, a first training set as a function of the at least a voice datum and the at least a query; and

generating, at a supervised machine-learning model, a communication output as a function of relating the at least a query to at least a textual output.

17. The method of claim 10 , wherein the method further comprises:

generating, by the computing device, a response datum as a function of the communication output; and

transmitting, by the computing device, the response datum to the remote device.

18. The method of claim 10 , wherein receiving the voice datum further comprises storing the voice datum, as a dataset, in a database.

19. The method of claim 18 , wherein the method further comprises training the machine learning process as a function of the voice datum dataset.

20. A system for voice recognition in autonomous flight of an electric aircraft, the system comprising:

a computing device communicatively connected to the electric aircraft, the computing device is configured to:

receive at least a voice datum from a remote device, wherein the voice datum is configured to include at least an expression datum;

extract at least a query from the at least a voice datum;

generate, using a machine-learning process, a communication output as a function of the at least a query; and

adjust a flight plan as a function of the communication output.