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<Paper uid="W97-0620">
  <Title>Dialogue Strategies for Improving the Usability of Telephone Human-Machine Communication</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Recognition errors and
</SectionTitle>
    <Paragraph position="0"> naturalness of dialogue State-of-the-art systems that receive their input by high-quality microphone have word accuracy scores above 90%. However, the recognition of spontaneous speech in telephone environment is below that rate. Actually, the telephone input of the recognizer may greatly differ from the uttered acoustic signal, due to the noisy environment of the call, and to the quality of the telephone microphone and propagation network. null Most current task-oriented applications of telephone human-machine dialogue are developed for being used by a large population of potential users. These applications require speaker independent real-time systems, and the opportunity of having training sessions with the system cannot be provided. The speaker independent recognizers designed for such applications assure the coverage of a great number of speakers, but some aspects of the speech modality of some users can induce the recognizer to make mistakes, especially in recognizing long sentences.</Paragraph>
    <Paragraph position="1"> The adverse recognition environment and the variability in user dependent features are the most frequent reasons of three kinds of recognition errors. The speech community usually classifies these errors into deletions, insertions, and substitutions. Some of them may be prevented by using language models during the recognition.</Paragraph>
    <Paragraph position="2"> At present, some approaches to language modeling take advantage of contextual information sent by the dialogue model. However, the task of the dialogue state dependent language modeling is more difficult in some application domains. For example, some of the task-oriented systems that give information about railway timetable, or flight scheduling, have large vocabularies that contain an huge number of words belonging to the same class: for example, Dialogos vocabulary is 3,500 words, including 2,983 proper names of places; another example is the</Paragraph>
  </Section>
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