SCARF: A Segmental Conditional Random Field Toolkit for Speech Recognition

Geoffrey Zweig and Patrick Nguyen

Abstract

This paper describes a new toolkit - SCARF - for doing speech

recognition with segmental conditional random fields. It is designed

to allow for the integration of numerous, possibly redundant

segment level acoustic features, along with a complete

language model, in a coherent speech recognition framework.

SCARF performs a segmental analysis, where each segment corresponds

to a word, thus allowing for the incorporation of acoustic

features defined at the phoneme, multi-phone, syllable and

word level. SCARF is designed to make it especially convenient

to use acoustic detection events as input, such as the detection

of energy bursts, phonemes, or other events. Language modeling

is done by associating each state in the SCRF with a state in

an underlying n-gram language model, and SCARF supports the

joint and discriminative training of language model and acoustic

model parameters. SCARF is available for download from

http://research.microsoft.com/en-us/projects/scarf/

Details

Publication typeInproceedings
PublisherInternational Speech Communication Association
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