PERSON NAME RECOGNITION IN ASR OUTPUTS USING CONTINUOUS CONTEXT MODELS

ABSTRACT The detection and characterization, in audiovisual documents, of speech utterances where person names are pronounced, is an important cue for spoken content analysis. This paper tackles the problematic of retrieving spoken person names in the 1-Best ASR outputs of broadcast TV shows. Our assumption is that a person name is a latent variable produced by the lexical context it appears in. Thereby, a spoken name could be derived from ASR outputs even if it has not been proposed by the speech recognition system. A new context modelling is proposed in order to capture lexical and structural information surrounding a spoken name. The fundamental hypothesis of this study has been validated on broadcast TV documents available in the context of the REPERE challenge

WHO REALLY SPOKE WHEN? FINDING SPEAKER TURNS AND IDENTITIES IN BROADCAST NEWS AUDIO

Automatic speaker segmentation and clustering methods have improved considerably over the last few years in the Broadcast News domain. However, these generally still produce locally consistent relative labels (such as spkr1, spkr2) rather than true speaker identities (such as Bill Clinton, Ted Koppel). This paper presents a system which attempts to find these true identities from the text transcription of the audio using lexical pattern matching, and shows the effect on performance when using state-of-the-art speaker clustering and speech-to-text transcription systems instead of manual references. 1

Towards a better integration of written names for unsupervised speakers identification in videos

International audienceExisting methods for unsupervised identification of speakers in TV broadcast usually rely on the output of a speaker diariza- tion module and try to name each cluster using names provided by another source of information: we call it "late naming". Hence, written names extracted from title blocks tend to lead to high precision identification, although they cannot correct er- rors made during the clustering step. In this paper, we extend our previous "late naming" ap- proach in two ways: "integrated naming" and "early naming". While "late naming" relies on a speaker diarization module op- timized for speaker diarization, "integrated naming" jointly op- timize speaker diarization and name propagation in terms of identification errors. "Early naming" modifies the speaker di- arization module by adding constraints preventing two clusters with different written names to be merged together. While "integrated naming" yields similar identification per- formance as "late naming" (with better precision), "early nam- ing" improves over this baseline both in terms of identification error rate and stability of the clustering stopping criterion