The use of long-term features for GMM- and i-vector-based speaker diarization systems
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[1] James R. Glass,et al. Exploiting Intra-Conversation Variability for Speaker Diarization , 2011, INTERSPEECH.
[2] Mark Huckvale,et al. How Is Individuality Expressed in Voice? An Introduction to Speech Production and Description for Speaker Classification , 2007, Speaker Classification.
[3] Paul Boersma,et al. Praat: doing phonetics by computer , 2003 .
[4] Jordi Luque,et al. Using voice-quality measurements with prosodic and spectral features for speaker diarization , 2015, INTERSPEECH.
[5] Sree Harsha Yella,et al. Speaker diarization of spontaneous meeting room conversations , 2015 .
[6] Jordi Luque,et al. Jitter and Shimmer Measurements for Speaker Diarization , 2014 .
[7] Sridha Sridharan,et al. i-vector Based Speaker Recognition on Short Utterances , 2011, INTERSPEECH.
[8] Patrick Kenny,et al. Bayesian Speaker Verification with Heavy-Tailed Priors , 2010, Odyssey.
[9] James R. Glass,et al. On the Use of Spectral and Iterative Methods for Speaker Diarization , 2012, INTERSPEECH.
[10] Christian A. Müller,et al. Prosodic and other Long-Term Features for Speaker Diarization , 2009, IEEE Transactions on Audio, Speech, and Language Processing.
[11] M.M. Homayounpour,et al. Speaker age interval and sex identification based on Jitters, Shimmers and Mean MFCC using supervised and unsupervised discriminative classification methods , 2006, 2006 8th international Conference on Signal Processing.
[12] Jordi Luque Serrano. Speaker diarization and tracking in multiple-sensor environments , 2012 .
[13] Hans Werner Strube,et al. Glottal-to-Noise Excitation Ratio - a New Measure for Describing Pathological Voices , 1997 .
[14] D Michaelis,et al. Selection and combination of acoustic features for the description of pathologic voices. , 1998, The Journal of the Acoustical Society of America.
[15] André Adami,et al. Modeling prosodic differences for speaker recognition , 2007, Speech Commun..
[16] Phuoc Nguyen. Automatic Speaker Classification Based on Voice Characteristics , 2011 .
[17] Jan Silovský,et al. Speaker diarization using PLDA-based speaker clustering , 2011, Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems.
[18] Jordi Luque,et al. Short- and Long-Term Speech Features for Hybrid HMM-i-Vector based Speaker Diarization System , 2016, Odyssey.
[19] Jordi Luque,et al. Improving i-Vector and PLDA Based Speaker Clustering with Long-Term Features , 2016, INTERSPEECH.
[20] Richard M. Stern,et al. Delta-spectral cepstral coefficients for robust speech recognition , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[21] Haizhou Li,et al. ALIZE 3.0 - open source toolkit for state-of-the-art speaker recognition , 2013, INTERSPEECH.
[22] Douglas A. Reynolds,et al. Diarization of Telephone Conversations Using Factor Analysis , 2010, IEEE Journal of Selected Topics in Signal Processing.
[23] Paul Boersma,et al. Praat, a system for doing phonetics by computer , 2002 .
[24] Jody Kreiman,et al. Perception of aperiodicity in pathological voice. , 2005, The Journal of the Acoustical Society of America.
[25] Xavier Anguera Miró,et al. Speaker Diarization For Multiple-Distant-Microphone Meetings Using Several Sources of Information , 2007, IEEE Transactions on Computers.
[26] Goutam Saha,et al. Performance comparison of speaker recognition systems in presence of duration variability , 2015, 2015 Annual IEEE India Conference (INDICON).
[27] Javier Hernando,et al. The Detection of Overlapping Speech with Prosodic Features for Speaker Diarization , 2011, INTERSPEECH.
[28] Mireia Farrús,et al. Jitter and shimmer measurements for speaker recognition , 2007, INTERSPEECH.
[29] Nicholas W. D. Evans,et al. Speaker Diarization: A Review of Recent Research , 2010, IEEE Transactions on Audio, Speech, and Language Processing.
[30] Daniel Garcia-Romero,et al. Speaker diarization with plda i-vector scoring and unsupervised calibration , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).
[31] Alfonso Ortega Giménez,et al. Robust diarization for speaker characterization (Diarización robusta para caracterización de locutores) , 2012 .
[32] S. Furui,et al. Cepstral analysis technique for automatic speaker verification , 1981 .
[33] Jean-François Bonastre,et al. Step-by-step and integrated approaches in broadcast news speaker diarization , 2006, Comput. Speech Lang..
[34] Margaret Lech,et al. Speaker Verification Based on Different Vector Quantization Techniques with Gaussian Mixture Models , 2009, 2009 Third International Conference on Network and System Security.
[35] Jordi Luque,et al. On the fusion of prosody, voice spectrum and face features for multimodal person verification , 2006, INTERSPEECH.
[36] Eduardo Lleida,et al. Variational Bayesian PLDA for speaker diarization in the MGB challenge , 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).
[37] Jan Silovský,et al. Speaker diarization of broadcast streams using two-stage clustering based on i-vectors and cosine distance scoring , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[38] Themos Stafylakis,et al. Efficient iterative mean shift based cosine dissimilarity for multi-recording speaker clustering , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[39] Pedro Gómez-Vilda,et al. The effectiveness of the glottal to noise excitation ratio for the screening of voice disorders. , 2010, Journal of voice : official journal of the Voice Foundation.
[40] Pedro Gómez Vilda,et al. Screening voice disorders with the glottal to noise excitation ratio , 2009 .
[41] Xi Li,et al. Stress and Emotion Classification using Jitter and Shimmer Features , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[42] Helen C. Shen,et al. Multiple hypothesis testing fusion method for multisensor systems , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).