Speaker Recognition Systems: A Tutorial

Abstract This paper gives an overview of speaker recognition systems. Speaker recognition is the task of automatically recognizing who is speaking by identifying an unknown speaker among several reference speakers using speaker-specific information included in speech waves. The different classification of speaker recognition and speech processing techniques required for performing the recognition task are discussed. The basic modules of a speaker recognition system are outlined and discussed. Some of the techniques required to implement each module of the system were discussed and others are mentioned. The methods were also compared with one another. Finally, this paper concludes by giving a few research trends in speaker recognition for some year to come

Articulatory-feature based sequence kernel for high-level speaker verification

Research has shown that articulatory feature-based phonetic-class pronunciation models (AFCPMs) can capture the pronunciation characteristics of speakers. However, the scoring method used in AFCPMs does not explicitly use the discriminative information available in the training data. To harness this information, this paper proposes converting speaker models to supervectors by stacking the discrete densities in AFCPMs. An AF-kernel is constructed from the supervectors of target speakers, background speakers, and claimants. An AF-kernel based SVM is then trained to classify the super-vectors. Results show that AF-kernel scoring is complementary to likelihood-ratio scoring, leading to better performance when the two scoring methods are combined.Department of Electronic and Information EngineeringRefereed conference pape

Effects of Equipment Variations on Speaker Recognition Error Rates

The purpose of this study was to examine the effects that equipment variation has on speaker recognition performance. Specifically microphone variation is investigated. The study examines the error rates of a speaker recognition system when microphones vary between the enrollment and testing phases. The study also examines the error rates of a speaker recognition system when microphones differ in similar environments and conditions. The metric for evaluation of effect is the false identity acceptance and the false identity rejection error rates.School of Electrical & Computer Engineerin

Phoneme Weighting and Energy-Based Weighting for Speaker Recognition

This dissertation focuses on determining specific vowel phonemes which work best for speaker identification and speaker verification, and also developing new algorithms to improve speaker identification accuracy. Results from the first part of our research indicate that the vowels /i/, /E/ and /u/ were the ones having the highest recognition scores for both the Gaussian mixture model (GMM) and vector quantization (VQ) methods (at most one classification error). For VQ, /i/, /I/, /e/, /E/ and /@/ had no classification errors. Persons speaking /E/, /o/ and /u/ have been verified well by both GMM and VQ methods in our experiments. For VQ, the verification results are consistent with the identification results since the same five phonemes performed the best and had less than one verification error. After determining several ideal vowel phonemes, we developed new algorithms for improved speaker identification accuracy. Phoneme weighting methods (which performed classification based on the ideal phonemes we found from the previous experiments) and other weighting methods based on energy were used. The energy weighting methods performed better than the phoneme weighting methods in our experiments. The first energy weighting method ignores the speech frames which have relatively small magnitude. Instead of ignoring the frames which have relatively small magnitude, the second method emphasizes speech frames which have relatively large magnitude. The third method and the adjusted third method are a combination of the previous two methods. The error reduction rate was 7.9% after applying the first method relative to a baseline system (which used Mel frequency cepstral coefficients (MFCCs) as feature and VQ as classifier). After applying the second method and the adjusted third method, the error reduction rate was 28.9% relative to a baseline system

Histogram equalization for robust text-independent speaker verification in telephone environments

Word processed copy. Includes bibliographical references

Contextual Social Networking

The thesis centers around the multi-faceted research question of how contexts may be detected and derived that can be used for new context aware Social Networking services and for improving the usefulness of existing Social Networking services, giving rise to the notion of Contextual Social Networking. In a first foundational part, we characterize the closely related fields of Contextual-, Mobile-, and Decentralized Social Networking using different methods and focusing on different detailed aspects. A second part focuses on the question of how short-term and long-term social contexts as especially interesting forms of context for Social Networking may be derived. We focus on NLP based methods for the characterization of social relations as a typical form of long-term social contexts and on Mobile Social Signal Processing methods for deriving short-term social contexts on the basis of geometry of interaction and audio. We furthermore investigate, how personal social agents may combine such social context elements on various levels of abstraction. The third part discusses new and improved context aware Social Networking service concepts. We investigate special forms of awareness services, new forms of social information retrieval, social recommender systems, context aware privacy concepts and services and platforms supporting Open Innovation and creative processes. This version of the thesis does not contain the included publications because of copyrights of the journals etc. Contact in terms of the version with all included publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]