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<Paper uid="H89-2035">
  <Title>References</Title>
  <Section position="3" start_page="0" end_page="263" type="intro">
    <SectionTitle>
2 APPROACH
</SectionTitle>
    <Paragraph position="0"> The obvious zero-order solution for this problem is to apply some rejection threshold on the word score. If the score reaches a level higher than the threshold then a new word is detected. However, when we examined the scores of words in a sentence, we found that the score of correct words varies widely, making it impossible to tell whether a word is correct or not. Therefore, this approach for detecting new words did not work well.</Paragraph>
    <Paragraph position="1"> Our proposed solution is to develop an explicit model of new words that will be detected whenever a new word occurs. The word model should be general enough to represent any new word. It should score better than other words in the vocabulary in place of new words only. It should not appear in place of already existing words in the vocabulary. We tried two acoustic models of new words which are described below.</Paragraph>
    <Paragraph position="2"> The first word model we tried was a new word model with a minimum 'of four phonemes long. It is a linear word model of 5 states and 4 identical phonemes with flat spectral distribution. The results were not encouraging due to the high false alarm rate and low detection rate.</Paragraph>
    <Paragraph position="3"> The second word model that we tried was a word model that allows for any sequence of phonemes of at least two phonemes long. The model has 3 states, all  phonemes in parallel from the first state to the second state, all phonemes in parallel from the second state to the third state and all phonemes in parallel looping on the second state. All phonemes are context independent phonemes. Note that this is in contrast to the normal vocabulary of the system, which uses context dependent phoneme models.</Paragraph>
    <Paragraph position="4"> We used a statistical class grammar to make the detection process more useful, and created a new word model for each open class. Open classes are the classes that accept new words (e.g. ship names, port names) as opposed to closed classes that do not accept new words (e.g. months, week-days, digits). By using separate new word models for the open classes we can make the distraction whether the new word was a ship name or a port name, etc. Also, it is easy to add the open class words to statistical class grammars and to Natural Language syntax and semantics.</Paragraph>
  </Section>
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