Abstract: A non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to receive audio data and provide the audio data as input to a first machine learning model to generate (1) transcription data, (2) timestamp data associated with the transcription data, and (3) a confidence metric associated with the transcription data. A speaking metric is calculated based on the transcription data and the timestamp data. The speaking metric and the confidence metric are provided as input to a second machine learning model to predict a listener effort metric.