Speech & natural language publications

We describe the development of our speech recognition system for the National Institute of Standards and Technology (NIST) Spring 2005 Meeting Rich Transcription (RT-05S) evaluation, highlighting improvements made since last…

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…

In this work, we describe a technique to augment a recognizer that uses this compromise with information from multiple-rate spectral models that emphasize either better time or better frequency resolution…

We investigate the use of the A* algorithm for joint segmentation and classification of dialog acts (DAs) of the ICSI Meeting Corpus. The proposed method is evaluated on both traditional…

A new speaker recognition system is described that uses Mel-frequency cepstral features. This system is a combination of four support vector machines (SVMs). All the SVM systems use polynomial features…

We describe the development of a speech recognition system for conversational telephone speech (CTS) that incorporates acoustic features estimated by multilayer perceptrons (MLPs). The acoustic features are based on frame-level…

We present a set of annotations of hierarchical topic segmentations and action item sub-dialogues collected over 65 meetings from the ICSI and ISL meeting corpora, designed to support automatic meeting…

We explore the use of adaptation transforms employed in speech recognition systems as features for speaker recognition. This approach is attractive because, unlike standard frame-based cepstral speaker recognition models, it…

We present a number of challenges and solutions that have arisen in the development of a speech translation system for American English and Pashto, highlighting those specific to a very…

We extend the POF algorithm to allow a more accurate way to select noisy-to-clean feature mappings, by allowing different combinations of speech and noise to have combination-specific mappings selected depending…

We present results from a study seeking to distinguish deceptive from non-deceptive speech using machine learning techniques on features extracted from a large corpus of deceptive and non-deceptive speech. We…

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…