Author: SRI International

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).

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.

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.

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.

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.

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.