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Automating Crowd-supervised Learning for Spoken Language Systems

Ian McGraw, Scott Cyphers, Panupong Pasupat, Jingjing Liu, and Jim Glass


Spoken language systems often rely on static speech recognizers. When the underlying models are improved on-the-fly, training is usually performed using unsupervised methods. In this work, we explore an alternative approach that uses human computation to provide crowd-supervised training of a deployed system. Although the framework we describe is applicable to any stochastic model for which the training data can be generated by non-experts, we demonstrate its utility on the lexicon and language model of a speech recognizer in a cinema voicesearch domain. We show how an initially shaky system can achieve over a 10% absolute improvement in word error rate (WER) – entirely without expert intervention. We then analyze how these gains were made.


Publication typeProceedings
PublisherInterspeech 2012
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