Search results for: “stolcke”

We highlight the improvements made to specific subsystems and analyze the performance of various subsystem combinations in different data conditions.

We describe the large vocabulary automatic speech recognition system developed for Modern Standard Arabic used for the 2007 GALE evaluation as part of the speech translation system.

We provide an overview of automatic higher-level systems and discuss potential advantages, as well as issues, for their use in the forensic context.

We investigate several feature normalization and scaling approaches for use in speaker verification based on support vector machines.

We address the problem of retrieving out-of-vocabulary (OOV) words/queries from audio archives for spoken term detection (STD) task. In this work, we employ hybrid recognition systems which contain both words and subword units (graphones) to generate hybrid lattice indexes.

We present a survey of the state of the art in voice-based speaker identification research. We describe the general framework of a text-independent speaker verification system, and, as an example, SRI’s voice-based speaker recognition system.

Detecting whether a talker is speaking his native language is useful for speaker recognition, speech recognition, and intelligence applications. We study the problem of detecting nonnative speakers of American English, using two standard speech corpora. We apply approaches effective in speaker verification to this task, including systems based on MLLR, phone N-gram, prosodic, and word…

We investigate the question of whether phone recognition models trained on large English databases can be used for speaker recognition in another language.

In this paper, we investigate the use of linguistically motivated and computationally efficient structured language models for reranking N-best hypotheses in a statistical machine translation system.

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.

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.

This paper describes the system developed jointly at SRI and OGI for participation in the 2006 NIST Spoken Term Detection (STD) evaluation.