word boundary- hypothesisation in hindi speech - Speech and ...

detected in a Hindi text. The results show that longer sequences detect more word
boundaries but they are also more error prone. Moreover, use of long sequences also
increases the uncertainty in the actual location of the word boundary. Hence, in
practice, this may not result in any significant improvement in lexical analysis, when
compared to the shorter sequences.
In the speech context, the lexical clues are to be applied on a symbol string
which may contain errors. Tests were conducted using sentences in which speech-like
errors were simulated, and the performance of the lexical clues was estimated. The
results show that the lexical clues are useful even for large percentage errors in the
input symbol string.
In addition to the language and lexical clues, one can also exploit prosodic and
acoustic-phonetic knowledge sources. In the study on the identification of prosodic
clues for word boundary hypothesisation, four prosodic features of pause, duration,
amplitude and pitch were considered. Of these, pauses are the simplest and most
reliable clues to word boundaries. In our study, it was found that pauses can be used to
detect only a few word boundaries, less than 20% of the total boundaries. It was also
found that a simple strategy of hypothesising a word boundary after every long vowel
results in the detection of many word boundaries (more than 70%) with errors less
than 25%. Pitch changes were also found useful to hypothesise word boundaries. In our
study, it was found that a drop in FO can be used to hypothesise a word boundary. This
detected more than 70% of the word boundaries with errors less than 30%. A
combination of pause, duration and pitch resulted in the detection of more than 75%
of the word boundaries with errors less than 15%.
In the study on the identification of acoustic-phonetic clues, two simple clues,
based on changes in the first formant were considered. The first clue uses changes in