14th International Congress of Phonetic Sciences (ICPhS-14)

San Francisco, CA, USA
August 1-7, 1999

Speech Recognition with Phonological Features

Aditi Lahiri

Department of Linguistics, University of Konstanz, Germany

The FUL (featurally underspecified lexicon) model of automatic speech recognition is based on the representation of words in the lexicon with underspecified distinctive features. The speech signal is converted from the waveform into an online spectral representation made up of formants and a few parameters describing the overall spectral shape. These LPC and spectral parameters are converted into distinctive phonological features which, in turn, are compared with all entries in the lexicon. No classification into segments, syllables, or spectral templates is used for the selection of words from the lexicon. Comparison of signal features with those stored in the lexicon uses a ternary system of matching, nomismatching, and mismatching features. Matching features increase the scoring for potential word candidates, no-mismatching features do not exclude candidates and only mismatching features lead to the rejection of word candidates. The word candidates are expanded to include word hypotheses, even without further acoustic evidence, and are used in the phonological and syntactic parsing that operates in parallel with the acoustic front-end.

Full Paper

Bibliographic reference.  Lahiri, Aditi (1999): "Speech recognition with phonological features", In ICPhS-14, 715-718.