Speech & natural language publications

We explore the two related tasks of dialog act (DA) segmentation and DA classification for speech from the ICSI Meeting Corpus. We employ simple lexical and prosodic knowledge sources, and…

In this paper, we present research toward ontology-based understanding of discourse in meetings and describe an ontology of multimodal discourse designed for this purpose.

This paper describes our recent efforts in exploring longer-range features and their statistical modeling techniques for speaker recognition. In particular, we describe a system that uses discriminant features from cepstral…

In this paper we provide a brief overview of research on structural metadata extraction in the DARPA EARS rich transcription program. Tasks include detection of sentence boundaries, filler words, and…

We describe a novel approach to modeling idiosyncratic prosodic behavior for automatic speaker recognition.

We investigate several variants of speech-rate-dependent acoustic models for large-vocabulary conversational speech recognition, in the framework of combining rate-specific models in decoding to compensate for speech rate variation.

We describe a new approach to modeling idiosyncratic prosodic behavior for automatic speaker recognition. The approach computes prosodic features by syllable, and models the syllable-feature sequences using support vector machines…

This paper describes a data collection process aimed at gathering human-computer dialogs in high-stress or “busy” domains where the user is concentrating on tasks other than the conversation, for example,…

We describe a state-of-the-art system for automatic detection of "metadata" in both broadcast news and spontaneous telephone conversations, developed as part of the DARPA EARS Rich Transcription program.

We investigate machine learning techniques for coping with highly skewed class distributions in two spontaneous speech processing tasks. Both tasks, sentence boundary and disfluency detection, provide important structural information for…

In this paper we investigate the use of morphology-based language models at different stages in a speech recognition system for conversational Arabic.

We describe the ICSI-SRI-UW team's entry in the Spring 2004 NIST Meeting Recognition Evaluation. The system was derived from SRI's 5xRT Conversational Telephone Speech (CTS) recognizer by adapting CTS acoustic…