Speech & natural language publications

In this paper, we present results on the Switchboard-2 corpus, where we compare 1-best phone decodings versus lattice phone decodings for the purposes of performing phonetic speaker recognition.

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…

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

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.

One of the major research thrusts in the speech group at ICSI is to use Multi-Layer Perceptron (MLP) based features in automatic speech recognition (ASR). This paper presents a study…

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…

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…

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.

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,…