8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Model-Driven Detection of Clean Speech Patches in Noise

Jonathan Laidler (1), Martin Cooke (1), Neil D. Lawrence (2)

(1) University of Sheffield, UK
(2) University of Manchester, UK

Listeners may be able to recognise speech in adverse conditions by "glimpsing" time-frequency regions where the target speech is dominant. Previous computational attempts to identify such regions have been source-driven, using primitive cues. This paper describes a model-driven approach in which the likelihood of spectro-temporal patches of a noisy mixture representing speech is given by a generative model. The focus is on patch size and patch modelling. Small patches lead to a lack of discrimination, while large patches are more likely to contain contributions from other sources. A "cleanness" measure reveals that a good patch size is one which extends over a quarter of the speech frequency range and lasts for 40 ms. Gaussian mixture models are used to represent patches. A compact representation based on a 2D discrete cosine transform leads to reasonable speech/background discrimination.

Full Paper

Bibliographic reference.  Laidler, Jonathan / Cooke, Martin / Lawrence, Neil D. (2007): "Model-driven detection of clean speech patches in noise", In INTERSPEECH-2007, 922-925.