Results 221 to 230 of about 12,356 (265)

GCC-Speaker: Target Speaker Localization with Optimal Speaker-Dependent Weighting in Multi-Speaker Scenarios

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
Wenju Liu, Jiangyan Yi
exaly   +2 more sources

Locality preserving speaker clustering

2009 IEEE International Conference on Multimedia and Expo, 2009
In this paper, we propose an efficient speaker clustering approach based on a locality preserving linear projective mapping in the Gaussian mixture model (GMM) mean supervector space. While the GMM mean supervector has turned out to be an effective representation of speakers, its dimensionality is usually very high.
Stephen M. Chu, Hao Tang 0001, Thomas S. Huang   +2 more
openaire   +2 more sources

Speaker localization with moving microphone arrays

2016 24th European Signal Processing Conference (EUSIPCO), 2016
Speaker localization algorithms often assume static location for all sensors. This assumption simplifies the models used, since all acoustic transfer functions are linear time invariant. In many applications this assumption is not valid. In this paper we address the localization challenge with moving microphone arrays. We propose two algorithms to find
Christine Evers, Yuval Dorfan, Sharon Gannot, Patrick A. Naylor   +3 more
openaire   +2 more sources

Automatic speaker localization based on speaker identification -A smart room application

Fourth International Conference on Information and Communication Technology and Accessibility (ICTA), 2013
This paper presents a system of speaker localization, based on the speaker recognition task. The main application could be the supervision of smart hospital-rooms, where the different crying patients (speaking patients) are localized thanks to their voice features inside the hospital.
Siham Ouamour-Sayoud, Halim Sayoud
openaire   +1 more source

Deep Audio-Visual Beamforming for Speaker Localization

IEEE Signal Processing Letters, 2022
Generalized Cross Correlation (GCC) is the most popular localization technique over the past decades and can be extended with the beamforming method e.g. Steered Response Power (SRP) when multiple microphone pairs exist. Considering the promising results of Deep Learning (DL) strategies over classical approaches, in this work, instead of directly using
Xinyuan Qian 0001, Qiquan Zhang, Guohui Guan 0001, Wei Xue 0002   +3 more
openaire   +2 more sources

Visual speaker localization aided by acoustic models

Proceedings of the 17th ACM international conference on Multimedia, 2009
The following paper presents a novel audio-visual approach for unsupervised speaker locationing. Using recordings from a single, low-resolution room overview camera and a single far-field microphone, a state-of-the art audio-only speaker localization system (traditionally called speaker diarization) is extended so that both acoustic and visual models ...
Friedland, Gerald, Yeo, Chuohao, Hung, Hayley   +2 more
openaire   +1 more source

Speaker localization in a reverberant environment

The 22nd Convention on Electrical and Electronics Engineers in Israel, 2002., 2003
The problem of speaker localization is addressed in this work. We present a novel approach for estimating the time difference of arrival (TDOA) of the speech signal to a microphone array, in a reverberant and noisy environment. By estimating acoustical transfer function (ATF) ratios, the TDOA is extracted from a relatively short impulse response.
T. Dvorkind, S. Gannot
openaire   +1 more source

Speaker recognition using local models

The Journal of the Acoustical Society of America, 2003
A system and method for voice recognition is disclosed. The system enrolls speakers using an enrollment voice samples and identification information. An extraction module (220) characterizes enrollment voice samples with high-dimensional feature vectors or speaker data points.
openaire   +4 more sources

Locality sensitive discriminant analysis for speaker verification

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016
In this paper, we apply Locality Sensitive Discriminant Analysis (LSDA) to speaker verification system for intersession variability compensation. As opposed to LDA which fails to discover the local geometrical structure of the data manifold, LSDA finds a projection which maximizes the margin between i-vectors from different speakers at each local area.
Danwei Cai, Weicheng Cai, Zhidong Ni, Ming Li 0026   +3 more
openaire   +2 more sources