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<Paper uid="H91-1049">
  <Title>A DYNAMICAL SYSTEM APPROACH TO CONTINUOUS SPEECH RECOGNITION</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> A new direction in speech recognition via statistical methods is to move from frame-based models, such as Hidden Markov Models (HMMs), to segment-based models that provide a better framework for modeling the dynamics of the speech production mechanism. The Stochastic Segment Model (SSM) is a joint model for a sequence of observations, allowing explicit modeling of time correlation. Originally in the SSM, a phoneme was modeled as a sequence of feature vectors that obeyed a multivariate Gaussian distribution. The variable length of an observed phoneme was handled either by modeling a fixed-length transformation of the observations \[6\] or by assuming the observation was a partially observed sample of a trajectory represented by a fixed-length model \[7\]. In the first case, the maximum likelihood estimates of the parameters can be obtained directly, but the Estimate-Maximize algorithm \[2\] may be required in the second case.</Paragraph>
    <Paragraph position="1"> Unfortunately, the joint Gaussian model suffers from estimation problems, given the number of acoustic features and the analysis-frame rate that modern continuous speech recognizers use. Therefore, a more constrained assumption about the correlation structure must be made. In previous work \[3\], we chose to constrain the model to a time-inhomogeneous Gauss-Markov process. Under the Gauss-Markov assumption, we were able to model well the time correlation of the first few cepstral coefficients, but the performance decreased when a larger number of features were</Paragraph>
  </Section>
class="xml-element"></Paper>