Enhancing Noisy Speech by Recursive Identification of Multiple Vocal Tract Models

M.Niranjan and W.J.Fitzgerald
Cambridge University Engineering Department
Trumpington Street
Cambridge CB2 1PZ


In our contribution to MaxEnt92 [1], we discussed Bayesian parameter estimation and model identification for a speech production model known as the Formant-Bandwidth model. This model is widely used in speech processing, in particular speech synthesis. Parameter estimation for such a Formant model is done recursively (i.e. sample by sample) by means of an extended Kalman filter algorithm [2]. It is possible to deal with the nonstationarity of speech signals, as the vocal tract moves, by running multiple Formant models in parallel [3,4]. The innovation probability in the Kalman filter may be interpreted as the Bayesian evidence for each model and used to calculate a model likelihood recursively.

In this paper, we extend the above work to an application of enhancing speech corrupted by additive noise. In this approach, multiple models of speech are recursively estimated in parallel. The models differ in the number of Formants in the speech and the noise covariances of the dynamical system. An estimate of the speech sample value and the model likelihood are evaluated for each model. The likelihoods are used to output a weighted sum of the predicted speech samples as the clean speech.

At the meeting, we will play a tape demonstrating the enhancement that can be achieved in this manner.


[1] Fitzgerald and Niranjan; ``Speech Processing using Bayesian Inference'', Proc. 12th International MaxEnt Workshop, Paris, July 1992.

[2] G.Rigoll; ``A new algorithm for estimation of formant trajectories directly from the speech signal based on an extended Kalman filter''; Proceedings of the ICASSP 1986, pp. 1229-1232.

[3] M.Niranjan, I.Cox and S.Hingorani; ``Recursive tracking of formants in speech signals'', Proc ICASSP 1994, Adelaide.

[4] Y.Bar-Shalom and T.E.Fortmann, Tracking and Data Association, Prentice Hall, 1988.

MaxEnt 94 Abstracts / mas@mrao.cam.ac.uk