Spectral features for automatic text-independent speaker recognition

Research Article. Ann Rev Resear. DOI: Go to Research Article Abstract Introduction Methods Application Details Experimental Results Discussion Conclusion References Abstract In this study, the performance of the prominent feature extraction and modeling methods in speaker recognition systems are evaluated on the specifically created database.

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Content:

• Spectral Features for Automatic Text-Independent Speaker Recognition
• Language and Text-Independent Speaker Recognition System Using Energy Spectrum and MFCCs
• Spectral Features for Automatic Text-Independent Speaker Recognition
• Improving speaker recognition by biometric voice deconstruction
• i-Vector-Based Speaker Verification on Limited Data Using Fusion Techniques
• Speaker recognition

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Spectral Features for Automatic Text-Independent Speaker Recognition

Brungart, Stefanie E. Kuchinsky, Megan M. Eitel, Sara M. Lippa, Tracey A. Brickell, Louis M. French, Rael T. Lange, Thomas F. Meissner, Virginie Woisard. Escalante-B, Veniamin I. Skerry-Ryan, Yonghui Wu. Nayem, Donald S. Mortensen, Michael R. Marlo, Graham Neubig. Kwon, Jagmohan Chauhan, Cecilia Mascolo. Johnson, Molly Babel. Rothkrantz, Joeri A. Zwerts, Jelle Treep, Casper S. The Phonetic Footprint of Covid? Klumpp, T. Bocklet, T. Arias-Vergara, J. Bayerl, J.

Kaandorp, Floor Meewis, Amparo C. Koot, Heysem Kaya. Han, Hanseok Ko. Bear, Veronica Morfi, Emmanouil Benetos. Abraham, V. Sivaramakrishnan, N.

Swapna, N. Dobson, Vaibhav A. Li, Colin T. Annand, Sarah Dugan, Sarah M. Schwab, Kathryn J. Riley, T. Douglas Mast. Escalante-B, Andreas Maier. Cox, Michael Akeroyd, John F. Robust wav2vec 2. Rocholl, Vicky Zayats, Daniel D. Walker, Noah B. Murad, Aaron Schneider, Daniel J. Black, Florian Metze. Broughton, Md. Asif Jalal, Roger K.

Kamble, Jose A. Peinado, Angel M. Gomez, Nicholas Evans, Maria A. Zuluaga, Massimiliano Todisco. Rozkovec, Z. Synchronic Fortition in Five Romance Languages? Speaking Corona? Pokorny, Katrin D. Should We Always Separate? Peter Hong, Chi-Chun Lee. Timothy Bunnell. Lin, Andy T. Rath, Abhishek Pandey. Lulich, Rita R. Khong, Sanjeev Khudanpur, Suzy J. Sahidullah, Denis Jouvet, Irina Illina. Exploring wav2vec 2. Ramesh, C. Shiva Kumar, K.

Sri Rama Murty. Mahadeva Prasanna. Sahidullah, Tomi Kinnunen. Prajapati, Dipesh K. Singh, Preet P. Amin, Hemant A. Mehta, Jinyu Li, Yifan Gong. Han, Shinji Watanabe. Prithvi Raj, Rohit Kumar, M. Basha Shaik. Mathad, Tristan J. Hustad, Julie Liss, Visar Berisha.

Revisiting Parity of Human vs. Wright, Mari Ostendorf. Ronny Huang, Tara N. Sainath, Ke Hu, Zelin Wu. Thiagarajan, Andreas Spanias. ThemePro 2. Gudupudi, Charles Bouveyron, Frederic Precioso. Henriques, Zeynep Akata, Samuel Albanie. N, Pinyi Wang, Bruno Bozza. Mortensen, Florian Metze, Shinji Watanabe. Strimel, Ariya Rastrow. Sainath, Ron J.

Tran, Kazuhito Koishida. Mahoor, Julia Madsen, Eshrat S. Lin, Hung-yi Lee. Garner, Alexandros Lazaridis. Ballard, Ricardo Gutierrez-Osuna. Schuller, Maja Pantic. Hershey, Nima Mesgarani, Zhuo Chen. Kachare, Prem C. Nataraj, Akshada Rathod, Sheetal K. Koenig, Susanne Fuchs. Acted vs. Emotion Recognition from Speech Using wav2vec 2.

Language and Text-Independent Speaker Recognition System Using Energy Spectrum and MFCCs

Speaker recognition is the identification of a speaker from features of his or her speech. This paper describes the use of decision tree induction techniques to induce classification rules that automatically identify speakers. Training times scale linearly with the population size. This paper describes the use of machine learning techniques to induce classification rules that automatically identify speakers. The most common application for speaker identification systems is in access control, for example, access to a room or privileged information over the telephone. Usually the task is simplified to speaker verification, where the speaker makes an identity claim and then the claim is either verified or rejected.

Spectral Features for Automatic Text-Independent Speaker Recognition

Spectral Features for Automatic Text. Based on a True Story … T. Why Study Feature Extraction? Studied Features 1. FFT-implemented filterbanks subband processing 2. FFT-cepstrum 3. LPC-derived features 4. Dynamic spectral features delta features. However, not consistently! Rectangular 2.

Improving speaker recognition by biometric voice deconstruction

Abstract 1. Introduction 2. Feature Extraction 3. Gaussian Mixture Model 4.

i-Vector-Based Speaker Verification on Limited Data Using Fusion Techniques

Brungart, Stefanie E. Kuchinsky, Megan M. Eitel, Sara M. Lippa, Tracey A. Brickell, Louis M.

Speaker recognition

Embed Size px x x x x Front-end or feature extractor is the first component in an automatic speakerrecognition system. Feature extraction transforms the raw speech signal intoa compact but effective representation that is more stable and discriminativethan the original signal. Since the front-end is the first component in thechain, the quality of the later components speaker modeling and patternmatching is strongly determined by the quality of the front-end. In otherwords, classification can be at most as accurate as the features. Several feature extraction methods have been proposed, and successfullyexploited in the speaker recognition task.

Springer Professional. Back to the search result list. Table of Contents. Hint Swipe to navigate through the chapters of this book Close hint.

Curator: Sadaoki Furui. Eugene M. Sadaoki Furui , Tokyo Institute of Technology. Speaker recognition is the process of automatically recognizing who is speaking by using the speaker-specific information included in speech waves to verify identities being claimed by people accessing systems; that is, it enables access control of various services by voice Furui, , , Applicable services include voice dialing, banking over a telephone network, telephone shopping, database access services, information and reservation services, voice mail, security control for confidential information, and remote access to computers. Another important application of speaker recognition technology is as a forensics tool.

Tomi Kinnunen Research seminar, Based on a True Story …. Based on a True Story … T. Why Study Feature Extraction? Principle of Feature Extraction.

In recent years, great progress has been made in the technical aspects of automatic speaker verification ASV. However, the promotion of ASV technology is still a very challenging issue, because most technologies are still very sensitive to new, unknown and spoofing conditions. Most previous studies focused on extracting target speaker information from natural speech. This paper aims to design a new ASV corpus with multi-speaking styles and investigate the ASV robustness to these different speaking styles.