ProfLifeLog: Environmental analysis and keyword recognition for naturalistic daily audio streams

This study presents keyword recognition evaluation on a new corpus named ProfLifeLog. ProfLifeLog is a collection of data captured on a portable audio recording device called the LENA unit. Each session in ProfLifeLog consists of 10+ hours of continuous audio recording that captures the work day of the speaker (person wearing the LENA unit). This study presents keyword spotting evaluation on the ProfLifeLog corpus using the PCN-KWS (phone confusion network-keyword spotting) algorithm [2]. The ProfLifeLog corpus contains speech data in a variety of noise backgrounds which is challenging for keyword recognition. In order to improve keyword recognition, this study also develops a front-end environment estimation strategy that uses the knowledge of speech-pause decisions and SNR (signal-to-noise ratio) to provide noise robustness. The combination of the PCN-KWS and the proposed front-end technique is evaluated on 1 hour of ProfLifeLog corpus. Our evaluation experiments demonstrate the effectiveness of the proposed technique as the number of false alarms in keyword recognition are reduced considerably.