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Jurafsky and Martins long-awaited text sets a new gold standard that will be difficult to surpass, as attested by the flurry of glowing reviews that accompanied its publication early this year.1 In a...
Modern speech recognizers rely on three core components: an acoustic model, a language model, and a pronunciation lexicon. In order to expand speech recognition capability to lowresource languages and...
This article is the second partof an updated version of the“MINDS 2006–2007 Reportof the Speech UnderstandingWorking Group,” one of five reports emanating from two workshops entitled “Meeting of the M...
In this paper we propose discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition tasks. After presenting our hierarchical modeling framework, we desc...
We present an approach to general multi-stream recognition utilizing multi-tape finite-state transducers (FSTs). The approach is novel in that each of the multiple streams” of features can represent ...
Articulatory feature models have been proposed in the automatic speech recognition community as an alternative to phone-based models ofspeech. In this paper, we extend this approach to the visual moda...
Most current speech recognizers use an observation space based on a temporal sequence of measurements extracted from fixed-length ‘‘frames’’ (e.g., Mel-cepstra). Given a hypothetical word or sub-word ...
BAUM-WELCH TRAINING FOR SEGMENT-BASED SPEECH RECOGNITION.
In this paper, we investigate the use of dynamic Bayesian networks (DBNs) to explicitly represent models of hidden features,such as articulatory or other phonological features, for automatic speech re...
In this research we explore multimodal speech recognition by augmenting acoustic information with that obtained by an ultrasonic emitter and receiver. After designing a hardware component to generate ...

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