In this paper we investigate the use of variable frame rate (VFR) analysis in automatic speech recognition (ASR). First,
we review VFR technique and analyze its behavior. It is experimentally shown that VFR improves ASR performance for
signals with low signal-to-noise ratios since it generates improved acoustic models and substantially reduces insertion
and substitution errors although it may increase deletion errors. It is also underlined that the match between the average
frame rate and the number of hidden Markov model states is critical in implementing VFR. Secondly, we analyze an
effective VFR method that uses a cumulative, weighted cepstral-distance criterion for frame selection and present a
revision for it. Lastly, the revised VFR method is combined with spectral- and cepstral-domain enhancement methods
including the minimum statistics noise estimation (MSNE) based spectral subtraction and the cepstral mean subtraction,
variance normalization and ARMA filtering (MVA) process. Experiments on the Aurora 2 database justify that VFR is
highly complementary to the enhancement methods. Enhancement of speech both facilitates the frame selection in VFR
and provides de-noised speech for recognition.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.