Ninth International Conference on Spoken Language Processing

Pittsburgh, PA, USA
September 17-21, 2006

An Assessment of Automatic Speech Recognition as Speech Intelligibility Estimation in the Context of Additive Noise

Wei M. Liu, John S. D. Mason, Nicholas W. D. Evans, Keith A. Jellyman

University of Wales Swansea, UK

This paper investigates the potential applicability of automatic speech recognition (ASR) and 6 well-reported objective quality measures for the task of ranking intelligibility of speech degraded by different real life background noises. In a recent investigation ASR has been reported to give high subjective correlation with human assessment when tested with various system degradations. This paper extends this investigation in two directions. First, the usefulness of the measures in the context of different real-life noises is considered. Second, the direct correspondence between statistics computed by an ASR system and human perceived intelligibility is assessed. Subjective listening tests are carried out to provide ground truth. Results show that ASR and WSS (weighted spectral slope) are the only two measures out of the seven considered to give good correlation with human opinion. Specially noted is performance of ASR with correlations ranging from 0.77 to 0.90.

Full Paper

Bibliographic reference.  Liu, Wei M. / Mason, John S. D. / Evans, Nicholas W. D. / Jellyman, Keith A. (2006): "An assessment of automatic speech recognition as speech intelligibility estimation in the context of additive noise", In INTERSPEECH-2006, paper 1191-Wed3FoP.10.