A Likelihood-Maximizing Framework for Enhanced In-Car Speech Recognition Based on Speech Dialog System Interaction - Digital Signal Processing for In-Vehicle Systems and Safety - page 149

Chapter 10
A Likelihood ‐ Maximizing Framework
for Enhanced In ‐ Car Speech Recognition
Based on Speech Dialog System Interaction
Tristan Kleinschmidt, Sridha Sridharan, and Michael Mason
Abstract Speech recognition in car environments has been identified as a valuable
means for reducing driver distraction when operating noncritical in-car systems.
Under such conditions, however, speech recognition accuracy degrades signifi-
cantly, and techniques such as speech enhancement are required to improve
these accuracies. Likelihood-maximizing (LIMA) frameworks optimize speech
enhancement algorithms based on recognized state sequences rather than traditional
signal-level criteria such as maximizing signal-to-noise ratio. LIMA frameworks
typically require calibration utterances to generate optimized enhancement
parameters that are used for all subsequent utterances. Under such a scheme,
suboptimal recognition performance occurs in noise conditions that are signifi-
cantly different from that present during the calibration session - a serious problem
in rapidly changing noise environments out on the open road. In this chapter, we
propose a dialog-based design that allows regular optimization iterations in order to
track the ever-changing noise conditions. Experiments using Mel-filterbank noise
subtraction (MFNS) are performed to determine the optimization requirements for
vehicular environments and show that minimal optimization is required to improve
speech recognition, avoid over-optimization, and ultimately assist with semi-
real-time operation. It is also shown that the proposed design is able to provide
improved recognition performance over frameworks incorporating a calibration
session only.
Keywords Automatic speech recognition (ASR) • In-car speech recognition
• LIMA frameworks • Mel-filterbank noise subtraction (MFNS)
T. Kleinschmidt ( * ) • S. Sridharan • M. Mason
Speech & Audio Research Laboratory, Queensland University of Technology,
Brisbane, QLD, Australia
e-mail: [e-mail] ; [e-mail] ; [e-mail]