Author: SRI International
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Catalyzing Network Expertise: Year 1 Report
The primary goal of this study is to examine change linked to new institutional pressures on schools brought about by the threat of sanctions under the federal No Child Left Behind Act
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Speaker Recognition with Session Variability Normalization Based on MLLR Adaptation Transforms
We present a new modeling approach for speaker recognition that uses the maximum-likelihood linear regression (MLLR) adaptation transforms employed by a speech recognition system as features for support vector machine (SVM) speaker models.
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Textual inference logic: take two
This note describes a logical system based on concepts and contexts, whose aim is to serve as a representation language for meanings of natural language sentences.
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Assimilating IT in the workplace: a study of situated learning
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Integrating MAP, Marginals, and Unsupervised Language Model Adaptation
We investigate the integration of various language model adaptation approaches for a cross-genre adaptation task to improve Mandarin ASR system performance on a recently introduced new genre, broadcast conversation (BC).
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Exploiting Information Extraction Annotations for Document Retrieval in Distillation Tasks
In this paper, we present our approach for using information extraction annotations to augment document retrieval for distillation. We take advantage of the fact that some of the distillation queries can be associated with annotation elements introduced for the NIST Automatic Content Extraction (ACE) task.
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fMPE-MAP: Improved Discriminative Adaptation for Modeling New Domains
This paper introduces a new adaptation approach, fMPE-MAP, which is an extension to the original fMPE (feature minimum phone error) algorithm, with the enhanced ability in porting Gaussian models and fMPE transforms to a new domain.
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Build IT: Girls Developing Information Technology Fluency Through Design. Annual Report Year 2
BuildIT is an after school and summer youth-based curriculum for low income middle school girls to develop IT fluency, interest in mathematics, and knowledge of IT careers.
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A Semi-Supervised Learning Approach for Morpheme Segmentation for an Arabic Dialect
We evaluate our approach by applying morpheme segmentation to the training data of a statistical machine translation (SMT) system. Experiments show that our approach is less sensitive to the availability of annotated stems than a previous rule-based approach and learns 12% more segmentations on our Iraqi Arabic data.
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Detecting Deception Using Critical Segments
We present an investigation of segments that map to GLOBAL LIES, that is, the intent to deceive with respect to salient topics of the discourse. We propose that identifying the truth or falsity of these CRITICAL SEGMENTS may be important in determining a speaker’s veracity over the larger topic of discourse.
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Duration and Pronunciation Conditioned Lexical Modeling for Speaker Verification
We propose a method to improve speaker recognition lexical model performance using acoustic-prosodic information. More specifically, the lexical model is trained using duration- and pronunciation-conditioned word N-grams, simultaneously modeling lexical information along with their acoustic and prosodic characteristics.
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Advances in Mandarin Broadcast Speech Recognition
We describe our continuing efforts to improve the UW-SRI-ICSI Mandarin broadcast speech recognizer.