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<Paper uid="H92-1069">
  <Title>Speaker-Independent Phone Recognition Using BREF</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> This paper reports on a series of experiments for speakerindependent, continuous speech phone recognition of French, using the recently recorded BREF corpus\[4, 6\]. BREF was designed to provide speech data for the development of dictation machines, the evaluation of continuous speech recognition systems (both speaker-dependent and speakerindependent), and to provide a large corpus of continuous speech to study phonological variations. These experiments are the first to use this corpus, and are meant to provide a baseline performance evaluation for vocabulary-independent (VI) phone recognition, as well as the development of a procedure for automatic segmentation and labeling of the corpus.</Paragraph>
    <Paragraph position="1"> First a brief description of BREF is given, along with the procedure for semi-automatic (verified) labeling and automatic segmentation of the speech data. The ability to accurately predict the phone labels from the text is assessed, as is the accuracy of the automatic segmentation. Next the phone recognition experiments performed using speech data from 62 speakers (43 for training, 19 for test) are described. A hidden Markov model (HMM) based recognizer has beeen evaluated with context-independent (CI) and context-dependent (CD) model sets, both with and without a duration model.</Paragraph>
    <Paragraph position="2"> Results are also given with and without the use of 1-gram and 2-gram statistics to provide phonotactic constraints. Preliminary VI word recognition results are presented with no grammar. The final section provides a discussion and summary, and a comparison of these results to the performance of other phone recognizers.</Paragraph>
  </Section>
class="xml-element"></Paper>