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<Paper uid="H90-1062">
  <Title>IMPROVED ACOUSTIC MODELING FOR CONTINUOUS SPEECH RECOGNITION</Title>
  <Section position="4" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> In the past few years there have been proposed a number of systems for large vocabulary, speaker-independent, continuous speech recognition which have achieved high word recognition accuracy \[1-5\]. The approach to large vocabulary speech recognition we adopt in this paper is a pattern recognition based approach. The basic speech units in the system use phonetic labels and are modeled acoustically based on a lexical description of words in the vocabulary. No assumption is made, a priori, about the mapping between acoustic measurements and sub-word linguistic units such as phonemes; such a mapping t Now with CSELT, Torino, Italy.</Paragraph>
    <Paragraph position="1"> is entirely learned via a finite training set of utterances. The resulting speech units, which we call phone-like units (PLU's) are essentially acoustic descriptions of linguistically-based units as represented in ttle words occurring in the given training set.</Paragraph>
    <Paragraph position="2"> In the baseline system reported in \[1\], acoustic modeling techniques for intra-word context-dependent PLU's were discussed. The focus of this paper is to extend the basic acoustic modeling techniques developed in \[1\] to include modeling of word juncture coarticulation and to incorporate higher-order time derivatives of cepstral and log energy parameters into the feature vector in order to improve speech recognition performance.</Paragraph>
    <Paragraph position="3"> We tested the improved acoustic modeling techniques on speaker-independent recognition of the DARPA Naval Resource Management task using both the word-pair (WP) and the no grammar (NG) conditions. For the FEB89 test set using the WP grammar, the word accuracy improved from 91.3% to 95.0% when both the inter-word context-dependent PLU's and an improved feature analysis were incorporated into the baseline system. We also observed that, for the first time, over 70% sentence accuracy was achieved. The same level of improvement was also obtained for the OCF89 and the JUN90 test sets.</Paragraph>
  </Section>
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