Electrical and Computer Engineering ETDs

Publication Date

Spring 5-2022


Speaker identification in noisy audio recordings, specifically those from collaborative learning environments, can be extremely challenging. There is a need to identify individual students talking in small groups from other students talking at the same time. To solve the problem, we assume the use of a single microphone per student group without any access to previous large datasets for training.

This dissertation proposes a method of speaker identification using cross-correlation patterns associated to an array of virtual microphones, centered around the physical microphone. The virtual microphones are simulated by using approximate speaker geometry observed from a video recording. The patterns are constructed based on estimates of the room impulse responses for each virtual microphone. The correlation patterns are then used to identify the speakers. The proposed method is validated with classroom audios and shown to substantially outperform diarization services provided by Google Cloud and Amazon AWS


TERMS Speaker Identification, Speaker Diarization, Audio Room Simulation, Virtual Microphone Arrays.

Document Type




Degree Name

Electrical Engineering

Level of Degree


Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Dr. Marios Pattichis Dr. Marios Pattichis

Second Committee Member

Dr. Ramiro Jordan

Third Committee Member

Dr. Sylvia Pattichis

Fourth Committee Member

Dr. Kim Linder

Fifth Committee Member

Dr. Manel Martinez-Ramon