Automated language identification during virtual conferences

1. A computer-implemented method, comprising:

accessing, by a computing device of a video conference provider system, audio information comprising an audio stream from a client device;

providing, by the computing device, a first audio segment from the audio stream to a language identification process of the computing device comprising a machine learning model that is trained to identify a language of a plurality of languages within recorded speech, wherein the plurality of languages comprises an unidentified language indicator and one or more languages, wherein the language identification process assigns a first confidence score to the first audio segment;

identifying, by the language identification process of the computing device, a first identified-language corresponding to the first audio segment based at least in part on the first confidence score exceeding a confidence threshold;

initiating, by the computing device, a change timer in response to identifying the first identified-language, wherein the confidence threshold is an increased confidence threshold until a conclusion of the change timer; and

before the conclusion of the change timer:

providing, by the computing device, a second audio segment from the audio stream to the language identification process of the computing device, wherein the language identification process assigns a second confidence score to the second audio segment; and

identifying, by the language identification process of the computing device, a second identified-language corresponding to the second audio segment based at least in part on the second confidence score exceeding the increased confidence threshold, wherein the first identified-language and the second identified-language are different languages.

2. The method of claim 1 , wherein the machine learning model generates a plurality of confidence score for the audio segment, wherein a corresponding confidence score is assigned for each language of the plurality of languages.

3. The method of claim 2 , wherein the plurality of confidence scores for the audio segment sum to one.

4. The method of claim 1 , wherein the providing further comprises:

sending, by the computing device, a notification containing the identified-language to the client device.

5. The method of claim 1 , further comprising:

providing the audio information to a selected transcription process, where the transcription process is selected, from a plurality of transcription processes, based at least in part on the identified-language.

6. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

one or more instructions that, when executed by one or more processors of a computing device of a video conference provider system, cause the computing device to:

access audio information comprising an audio stream from a client device;

provide a first audio segment from the audio stream to a language identification process of the computing device comprising a machine learning model that is trained to identify a language of a plurality of languages within recorded speech, wherein the plurality of languages comprises an unidentified language indicator and one or more languages, wherein the language identification process assigns a first confidence score to the first audio segment;

identify a first identified-language corresponding to the first audio segment based at least in part on the first confidence score exceeding a confidence threshold;

initiate a change timer in response to identifying the first identified-language, wherein the confidence threshold is an increased confidence threshold until a conclusion of the change timer; and

before the conclusion of the change timer:

provide a second audio segment from the audio stream to the language identification process, wherein the language identification process assigns a second confidence score to the second audio segment; and

identify, by the language identification process, a second identified-language corresponding to the second audio segment based at least in part on the second confidence score exceeding the increased confidence threshold, wherein the first identified-language and the second identified-language are different languages.

7. The non-transitory computer-readable medium of claim 6 , wherein the machine learning model generates a plurality of confidence score for the audio segment, wherein a corresponding confidence score is assigned for each language of the plurality of languages.

8. The non-transitory computer-readable medium of claim 7 , wherein the plurality of confidence scores for the audio segment sum to one.

9. The non-transitory computer-readable medium of claim 6 , wherein the providing further comprises:

send a notification containing the identified-language to the client device.

10. The non-transitory computer-readable medium of claim 6 , wherein the one or more instructions further cause the computing device to:

provide the audio information to a selected transcription process, where the transcription process is selected, from a plurality of transcription processes, based at least in part on the identified-language.

11. A computing device, comprising:

one or more memories; and

one or more processors, communicatively coupled to the one or more memories, configured to:

access audio information comprising an audio stream from a client device;

provide a first audio segment from the audio stream to a language identification process of a computing device of a video conference provider system comprising a machine learning model that is trained to identify a language of a plurality of languages within recorded speech, wherein the plurality of languages comprises an unidentified language indicator and one or more languages, wherein the language identification process assigns a first confidence score to the first audio segment;

identify a first identified-language corresponding to the first audio segment based at least in part on the first confidence score exceeding a confidence threshold;

initiate a change timer in response to identifying the first identified-language, wherein the confidence threshold is an increased confidence threshold until a conclusion of the change timer; and

before the conclusion of the change timer:

providing a second audio segment from the audio stream to the language identification process of the computing device, wherein the language identification process assigns a second confidence score to the second audio segment; and

identify, by the language identification process, a second identified-language corresponding to the second audio segment based at least in part on the second confidence score exceeding the increased confidence threshold, wherein the first identified-language and the second identified-language are different languages.

12. The computing device of claim 11 , wherein the machine learning model generates a plurality of confidence score for the audio segment, wherein a corresponding confidence score is assigned for each language of the plurality of languages.

13. The computing device of claim 12 , wherein the plurality of confidence scores for the audio segment sum to one.

14. The computing device of claim 11 , wherein the providing further comprises:

send a notification containing the identified-language to the client device.

15. The computing device of claim 11 , wherein the one or more processors are further configured to:

provide the audio information to a selected transcription process, where the transcription process is selected, from a plurality of transcription processes, based at least in part on the identified-language.