Prof-Life-Log: Monitoring and assessment of human speech and acoustics using daily naturalistic audio streams

Speech technology advancements have progressed significantly in the last decade, yet major research challenges continue to impact effective advancements for diarization in naturalistic environments. Traditional diarization efforts have focused on single audio streams based on telephone communications, broadcast news, and/or scripted speeches or lectures. Limited effort has focused on extended naturalistic data. Here, algorithm advancements are established for an extensive daily audio corpus called Prof-Life-Log, consisting of + 80days of 8-16 hr recordings from an individual’s daily life. Advancements include the formulation of (i) an improved threshold-optimized multiple feature speech activity detector (TO-Combo-SAD), (ii) advanced primary vs. secondary speaker detection, (iii) advanced word-count system using part-of-speech tagging and bag-of-words construction, (iv) environmental “sniffing” advancements to identify location based on properties of the acoustic space, and (v) diarization interaction analysis which highlights the amount of speech each individual produces along with recipient direction. The diarization advancements are evaluated on CRSS-UTDallas Prof-Life-Log. Results show improvements in speech activity detection, word count estimation, and environmental sniffing for naturalistic audio streams. The advancements suggest improved speech/language processing algorithms can address the increased diversity in daily audio for speaker knowledge detection/estimation and summarization.