Digital Signal Processing Reference
In-Depth Information
Chapter 18
ROBUST ASR INSIDE A VEHICLE USING BLIND
PROBABILISTIC BASED UNDER-DETERMINED
CONVOLUTIVE MIXTURE SEPARATION
TECHNIQUE
Shubha Kadambe
HRL Laboratories, LLC, 3011 Malibu Canyon Rd., Malibu, CA 91320, USA;
Email: skadambe@hrl.com
Abstract:
Spoken dialogue based information retrieval systems are being used in mobile
environments such as cars. However, the car environment is noisy and the
user's speech signal gets corrupted due to dynamically changing acoustic
environment and the number of interference signals inside the car. The
interference signals get mixed with speech signals convolutively due to the
chamber impulse response. This tends to degrade the performance of a speech
recognition system which is an integral part of a spoken dialogue based
information retrieval system. One solution to alleviate this problem is to
enhance speech signals such that the recognition accuracy does not degrade
much. In this Chapter, we describe a blind source separation technique that
would enhance convolutively mixed speech signals by separating the
interference signals from the genuine speech. This technique is applicable for
under-determined case i.e., the number of microphones is less than the number
of signal sources and uses a probabilistic approach in a sparse transformed
domain. We have collected speech data inside a car with variable number of
interference sources such as wipers on, radio on, A/C on. We have applied our
blind convolutive mixture separation technique to enhance the mixed speech
signals. We conducted experiments to obtain speech recognition accuracy
using with and without enhanced speech signals. For these experiments we
used a continuous
speech
recognizer. Our results indicate
15-35 %
improvement in speech recognition accuracy.
Keywords:
Blind source separation, convolutive mixture, under determined, signal
enhancement, speech recognition accuracy.
