Topic: “A Short-Time Speech-Based Speech Transmission Index”
Several algorithms have been shown to generate a metric corresponding to the Speech Transmission Index (STI) using speech as a probe stimulus (e.g. Goldsworthy & Greenberg, J. Acoust. Soc. Am., 116, 3679-3689, 2004). The time-domain approaches work well on long speech segments and have the added potential to be used for short-time analysis. This study investigates the performance of the Envelope Regression (ER) time-domain STI method as a function of window length, in acoustically degraded environments with multiple talkers and speaking styles. The ER method is compared with a short-time Theoretical STI, derived from octave-band signal-to-noise ratios and reverberation times. The metric is also compared to intelligibility scores on conversational speech and speech articulated clearly but at normal speaking rates (Clear/Norm) in stationary noise. Finally, an analysis is presented that investigates sources and prevention of ER deviations from the Theoretical STI when window lengths are reduced below 0.3 s.
Dr. Karen Payton is a faculty member and Graduate Program Director of the Electrical and Computer Engineering Department at UMass Dartmouth. She earned her BS in Electrical and Biomedical Engineering at Carnegie Mellon University and her MS and PhD in Electrical Engineering from the Johns Hopkins University. She spent three years as a post-doctoral fellow at MIT’s Research Laboratory of Electronics and maintains a visiting scientist position there.