Newsgroups: comp.speech
Path: pavo.csi.cam.ac.uk!pipex!uknet!cam-eng!jn106
From: jn106@eng.cam.ac.uk (J.A. Nolazco Flores)
Subject: Technical Report Available
Sender: jn106@eng.cam.ac.uk (J.A. Nolazco Flores)
Message-ID: <1993Jun22.111908.8109@eng.cam.ac.uk>
Date: Tue, 22 Jun 1993 11:19:08 GMT
Nntp-Posting-Host: dsl.eng.cam.ac.uk
Organization: Cambridge University Engineering Department, UK
Lines: 92

The following technical report is available by anonymous ftp from the
archive of the Speech, Vision and Robotics Group at the Cambridge
University Engineering Department.

       CSS-PMC: A CCOMBINED ENHANCEMENT/COMPENSATION SCHEME 
           FOR CONTINUOUS SPEECH RECOGNITION IN NOISE

                 J.A. Nolazco Flores & S.J. Young

              Technical Report CUED/F-INFENG/TR.128

		Cambride University Engineering Department
			Trumpington Street
                        Cambridge CB2 1PZ
                             England


			     Abstract

Training HMMs on the same conditions as in recognition makes models
learn not only the features of the speech, but also those of the
environment.  However, attempting to produce models for all possible
environments is impractical. One way to solve this problem is to
compensate models trained on clean speech to give ``artificially''
adapted models. The goal of these noise adaptation techniques is to
reach the same recognition performance as would be obtained by
training in the noisy conditions.

However, even training in noise can only achieve limited recognition
performance because the high variance at low SNR makes the features
begin to overlap thereby reducing discrimination. The problem is even
worse when the vocabulary grows. In order to improve recognition
performance in very noisy environments, speech enhancement techniques
must be useful.  Enhancement schemes can improve the SNR, minimise the
variance, and emphasise the important features of the signal, but at
the expense of signal distortion. Minimising both signal distortion
and noise, a signal with better features and lower variability is
obtained.

In our earlier work, speech models were adapted to a signal enhanced
by spectral subtraction using Parallel Model Compensation (PMC) in a
scheme called SS-PMC. Although very good performance was demonstrated
for the SS-PMC scheme, it does require a explicit word boundary
detector and this limits its use in practice. In order to avoid this
drawback, a Continuous Spectral Subtraction(CSS) scheme has been
developed.


In this new system, speech models are adapted for a signal enhanced by
this CSS scheme. It will be shown that the enhanced signal after being
processed by the CSS can be represented by the addition of the noisy
speech plus a correction term in the linear domain. SS-PMC transforms
the noise and speech model parameters from the cepstral domain to the
linear domain, adds these parameters and the SS correction term, and
then creates an adapted model by returning to the cepstral domain.
Therefore, SS-PMC can be modified to compensate for the correction
term in the linear domain. This modified version of SS-PMC will be
called the CSS-PMC method.

The results obtained by the CSS-PMC technique are very encouraging,
showing that it is very effective to use adaptation techniques to
compensate for the signal distortion which is a side effect of a
CSS-based enhancement scheme.


************************ How to obtain a copy ************************

a) Via FTP:

unix> ftp svr-ftp.eng.cam.ac.uk
Name: anonymous
Password: (type your email address)
ftp> cd reports
ftp> binary
ftp> get nolazco_tr128.ps.Z
ftp> quit
unix> uncompress nolazco_tr128.ps.Z
unix> lpr nolazco_tr128.ps (or however you print PostScript)

b) Via postal mail:

Request a hardcopy from

J. A. Nolazco Flores,
Cambridge University Engineering Department, 
Trumpington Street, 
Cambridge CB2 1PZ,
England.

or email me: jn106@eng.cam.ac.uk


