Author: SRI International

In this paper, we describe a general technique for optimizing the relative weights of feature sets in a support vector machine (SVM) and show how it can be applied to the field of speaker recognition. Our training procedure uses an objective function that maps the relative weights of the feature sets directly to a classification…

We ask if active learning with lexical cues can help for this task and this domain. To better address this question, we explore active learning for two different types of DA models — hidden Markov models (HMMs) and maximum entropy (maxent).

We compare a generative hidden Markov model (HMM)-based approach and two conditional models — a maximum entropy (Maxent) model and a conditional random field (CRF) — for detecting disfluencies in speech. The conditional modeling approaches provide a more principled way to model correlated features.

We present an efficient discriminative training procedure utilizing phone lattices. Different approaches to expediting lattice generation, statistics collection, and convergence were studied.

We report on results of two experiments designed to compare subjects’ ability to extract information from audio recordings of conversational telephone speech (CTS) with their ability to extract information from text transcripts of these conversations, with and without the ability to hear the audio recordings.

In this work, we are presenting a class-based score combination technique that relies on clustering of both the target models and the test utterances in a vector space defined by a set of speaker-specific transformation parameters estimated during transcription of the talker.