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We present our newly developed real-time conversation analyzer for group meetings. The goal of the system is to estimate automatically “who speaks when and what” in an online manner. In our system, “who speaks when” information is first obtained by estimating the directions of arrival (DOAs) of signals. Then, “who speaks what” is estimated with our automatic speech recognition (ASR) system, after...
This paper proposes a microphone array-based speech activity detection (SAD) method for analyzing multi-party conversations recorded in the presence of noise. In particular, the proposed method considers conversations where the number of speakers and speaker locations cannot be restricted, such as when standing and talking, and at poster sessions. When we observe such conversations, there are directional...
We present a probabilistic speaker clustering and diarization model. Speaker diarization determines ldquowho spoke whenrdquo from the recorded conversation of unknown number of people. We formulate this problem as the clustering of sequential auditory features generated by an unknown number of latent mixture components (speakers). We employ a probabilistic model which automatically estimates the number...
This paper presents a speaker diarization system that estimates who spoke when in a meeting. Our proposed system is realized by using a noise robust voice activity detector (VAD), a direction of arrival (DOA) estimator, and a DOA classifier. Our previous system utilized the generalized cross correlation method with the phase transform (GCC-PHAT) approach for the DOA estimation. Because the GCC-PHAT...
This paper presents a speaker indexing method that uses a small number of microphones to estimate who spoke when. Our proposed speaker indexing is realized by using a noise robust voice activity detector (VAD), a QCC-PHAT based direction of arrival (DOA) estimator, and a DOA classifier. Using the estimated speaker indexing information, we can also enhance the utterances of each speaker with a maximum...
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