Add to ORKGThe following paper presents an application that fuses the currently artificially separated tasks of speaker identification and speaker diarization. The presented method allows online identification of who is currently speaking using a single far-field microphone in a meeting scenario. It is able to recognize the current speaker after any two seconds of speech. An evaluation of the robustness of the algorithm using the AMI Meeting Corpus resulted in a Diarization Error Rate of 12.67 %. Index Terms — application, speaker diarization, speaker identification, online 1
Speaker diarization is originally defined as the task of de-termining “who spoke when ” given an aud...
In this paper, we investigate new approaches to improve speech activity detection, speaker segmentat...
This work aims at a task of speaker diarization. The goal is to implement a system which is able to ...
The paper proposes a speaker identification scheme for a meeting scenario, that is able to answer th...
Abstract-- Human-Machine interaction in meetings requires the localization and identification of the...
Abstract—Speaker diarization is the task of determining “who spoke when? ” in an audio or video reco...
This paper presents our newly developed real-time meeting analyzer for monitoring conversations in a...
International audienceThis paper introduces a new task termed low-latency speaker spotting (LLSS). R...
The paper concentrates on speaker diarization over meeting recordings. The task of speaker diarizati...
This paper introduces a collaborative personal speaker identification system to annotate conversatio...
Speaker diarization is the problem of determining "who spoke when" in an audio recording when the nu...
Automatic speech recognition is more and more widely and effectively used. Nevertheless, in some aut...
Ambient Inteligence aims to create smart spaces providing services in a transparent and non-intrusiv...
On-line speaker diarization aims to detect “who is speaking now" in a given audio stream. The majori...
Speaker diarization systems process audio files by labelling speech segments according to speakers' ...
Speaker diarization is originally defined as the task of de-termining “who spoke when ” given an aud...
In this paper, we investigate new approaches to improve speech activity detection, speaker segmentat...
This work aims at a task of speaker diarization. The goal is to implement a system which is able to ...
The paper proposes a speaker identification scheme for a meeting scenario, that is able to answer th...
Abstract-- Human-Machine interaction in meetings requires the localization and identification of the...
Abstract—Speaker diarization is the task of determining “who spoke when? ” in an audio or video reco...
This paper presents our newly developed real-time meeting analyzer for monitoring conversations in a...
International audienceThis paper introduces a new task termed low-latency speaker spotting (LLSS). R...
The paper concentrates on speaker diarization over meeting recordings. The task of speaker diarizati...
This paper introduces a collaborative personal speaker identification system to annotate conversatio...
Speaker diarization is the problem of determining "who spoke when" in an audio recording when the nu...
Automatic speech recognition is more and more widely and effectively used. Nevertheless, in some aut...
Ambient Inteligence aims to create smart spaces providing services in a transparent and non-intrusiv...
On-line speaker diarization aims to detect “who is speaking now" in a given audio stream. The majori...
Speaker diarization systems process audio files by labelling speech segments according to speakers' ...
Speaker diarization is originally defined as the task of de-termining “who spoke when ” given an aud...
In this paper, we investigate new approaches to improve speech activity detection, speaker segmentat...
This work aims at a task of speaker diarization. The goal is to implement a system which is able to ...