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<Paper uid="H94-1066">
  <Title>SIGNAL PROCESSING FOR ROBUST SPEECH RECOGNITION</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> A continuing problem with current speech recognition technology is that of lack of robustness with respect to environmental variability. For example, the use of microphones other than the ARPA standard Sennheiser HM--414 &amp;quot;close-talking&amp;quot; headset (CLSTLK) severely degrades the performance of systems like the original SPHINX system, even in a relatively quiet office environment \[e.g. 1,2\]. Applications such as speech recognition in automobiles, over telephones, on a factory floor, or outdoors demand an even greater degree of environmental robustness.</Paragraph>
    <Paragraph position="1"> In this paper we describe and compare the performance of a series of cepstrum-based procedures that enable the CMU SPHINX-II \[8\] speech recognition system to maintain a high level of recognition accuracy over a wide variety of acoustical environments. We also discuss the aspects of these algorithms that appear to have contributed most significantly to the success of the SPHINX-II system in the 1993 ARPA CSR evaluations for microphone independence (Spoke 5) and calibrated noise sources (Spoke 8).</Paragraph>
    <Paragraph position="2"> In previous years we described the performance of cepstral mapping procedures such as the CDCN algorithm, which is effective but fairly computationally costly \[2\]. More recently we discussed the use of eepstral highpass-filtering algorithms \[such as the populax RASTA and cepstral-mean-normalization algorithms (CMN) \[6\]. These algorithms are very simple to implement but somewhat limited in effectiveness, and CMN is now part of baseline processing for the CMU and many other systems.</Paragraph>
    <Paragraph position="3"> In this paper we describe several new procedures that when used in consort can provide as much as an additional 40 percent improvement over baseline processing with CMN. These tech-</Paragraph>
  </Section>
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