Communication issue detection using evaluation of multiple machine learning models

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured to implement the following steps:
• applying, by a communication issue detector, a representation of an audio signal associated with a communication to:
• (i) a trigger word analysis module that determines if one or more trigger words are detected in the audio signal from the audio signal using a trained trigger word detection model that is trained using (a) a set of trigger words indicative of a technical device issue with one or more devices associated with the communication and (b) contextual information comprising one or more of a nearby additional trigger word feature, a time-in-meeting of trigger word feature and a before/after silence pattern feature, relative to a user-provided feedback score;
• and
• (ii) an audio activity pattern analysis module that determines if a silence pattern anomaly is detected in the audio signal using a trained audio activity model, wherein the audio activity pattern analysis module evaluates a length of an audio activity portion of the audio signal relative to a length of a silence portion of the audio signal to identify the silence pattern anomaly, wherein the silence pattern anomaly is indicative of a technical device issue with one or more devices associated with the communication;
• combining results of the trigger word analysis module and the audio activity pattern analysis module to identify a communication issue for the communication;
• and
• implementing one or more remedial actions responsive to the identification of the communication issue.

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:

• applying, by a communication issue detector, a representation of an audio signal associated with a communication to:
• (i) a trigger word analysis module that determines if one or more trigger words are detected in the audio signal from the audio signal using a trained trigger word detection model that is trained using (a) a set of trigger words indicative of a technical device issue with one or more devices associated with the communication and (b) contextual information comprising one or more of a nearby additional trigger word feature, a time-in-meeting of trigger word feature and a before/after silence pattern feature, relative to a user-provided feedback score;
• and
• (ii) an audio activity pattern analysis module that determines if a silence pattern anomaly is detected in the audio signal using a trained audio activity model, wherein the audio activity pattern analysis module evaluates a length of an audio activity portion of the audio signal relative to a length of a silence portion of the audio signal to identify the silence pattern anomaly, wherein the silence pattern anomaly is indicative of a technical device issue with one or more devices associated with the communication;
• combining results of the trigger word analysis module and the audio activity pattern analysis module to identify a communication issue for the communication;
• and
• implementing one or more remedial actions responsive to the identification of the communication issue.