This paper presents a novel method to extract Speech Transmission Index (STI) from reverberated speech utterances using an artificial neural network. The convolutions of anechoic speech signals and simulated impulse responses of rooms of various kinds are used to train the artificial neural network. A time to frequency domain transformation algorithm is proposed as the pre-processor. A multi-layered feed forward neural network trained by back-propagation is adopted. Once trained, the neural network can accurately estimate Speech Transmission Index from speech signals received by a microphone in rooms. This approach utilises a naturalistic sound source, speech, and hence has potential to facilitate occupied measurement.

Authors: Cox, Trevor; Li, Francis
Affiliation: School of Acoustics and Electronic Engineering, University of Salford, Salford, Greater Manchester, UK
AES Convention: 110 (May 2001) Paper Number: 5354
Publication Date: May 1, 2001
Subject: Room Acoustics & Sound Reinforcement

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