Study of Speaker Recognition Systems

Speaker Recognition is the computing task of validating a user’s claimed identity using characteristics extracted from their voices. This technique is one of the most useful and popular biometric recognition techniques in the world especially related to areas in which security is a major concern. It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities. Speaker recognition can be classified into identification and verification. Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker verification, on the other hand, is the process of accepting or rejecting the identity claim of a speaker. The process of Speaker recognition consists of 2 modules namely: - feature extraction and feature matching. Feature extraction is the process in which we extract a small amount of data from the voice signal that can later be used to represent each speaker. Feature matching involves identification of the unknown speaker by comparing the extracted features from his/her voice input with the ones from a set of known speakers. Our proposed work consists of truncating a recorded voice signal, framing it, passing it through a window function, calculating the Short Term FFT, extracting its features and matching it with a stored template. Cepstral Coefficient Calculation and Mel frequency Cepstral Coefficients (MFCC) are applied for feature extraction purpose. VQLBG (Vector Quantization via Linde-Buzo-Gray), DTW (Dynamic Time Warping) and GMM (Gaussian Mixture Modelling) algorithms are used for generating template and feature matching purpose

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Real time speaker recognition using MFCC and VQ

Speaker Recognition is a process of automatically recognizing who is speaking on the basis of the individual information included in speech waves. Speaker Recognition is one of the most useful biometric recognition techniques in this world where insecurity is a major threat. Many organizations like banks, institutions, industries etc are currently using this technology for providing greater security to their vast databases.Speaker Recognition mainly involves two modules namely feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the speaker’s voice signal that can later be used to represent that speaker. Feature matching involves the actual procedure to identify the unknown speaker by comparing the extracted features from his/her voice input with the ones that are already stored in our speech database.In feature extraction we find the Mel Frequency Cepstrum Coefficients, which are based on the known variation of the human ear’s critical bandwidths with frequency and these, are vector quantized using LBG algorithm resulting in the speaker specific codebook. In feature matching we find the VQ distortion between the input utterance of an unknown speaker and the codebooks stored in our database. Based on this VQ distortion we decide whether to accept/reject the unknown speaker’s identity. The system I implemented in my work is 80% accurate in recognizing the correct speaker.In second phase we implement on the acoustic of Real Time speaker ecognition using mfcc and vq on a TMS320C6713 DSP board. We analyze the workload and identify the most timeconsuming operations

Speaker tracking system using speaker boundary detection

This thesis is about a research conducted in the area of Speaker Recognition. The application is concerned to the automatic detection and tracking of target speakers in meetings, conferences, telephone conversations and in radio and television broadcasts. A Speaker Tracking system is developed here, in collaboration with the Center for Language and Speech Technologies and Applications (TALP) in UPC. The main objective of this Speaker Tracking system is to answer the question: When the target speaker speaks? The system uses training speech data for the target speaker in the pre-enrollment stage. Three main modules have been designed for this Speaker Tracking system. In the first module an energy based Speech Activity Detection is applied to select the speech parts of the audio. In the second module the audio is segmented according to the speaker turning points. In the last module a Speaker Verification is implemented in which the target speakers are verified and tracked. Two different approaches are applied in this last module. In the first approach for Speaker Verification, the target speakers and the segments are modeled using the state-of-the-art, Gaussian Mixture Models (GMM). In the second approach for Speaker Verification, the identity vectors (i-vectors) representation is applied for the target speakers and the segments. Finally, the performance of both these approaches is compared for the results evaluation

An Analytic investigation into self organizing maps and their network topologies

This paper details master\u27s thesis work involving research and investigation into the approach of self-organizing maps for clustering of data, more specifically, clustering of image data, and how this can be used in understanding image composition. This work will build upon ideas which have previously been explored, such as using self organizing maps for identifying and grouping different regions of an image which may possess similar features. A large part of this research is based upon experimentation with a variety of topological models of the self-organizing map network and investigation into what advantages these different topologies afford the network in terms of its clustering capabilities

Application of shifted delta cepstral features for GMM language identification

Spoken language identifcation (LID) in telephone speech signals is an important and difficult classification task. Language identifcation modules can be used as front end signal routers for multilanguage speech recognition or transcription devices. Gaussian Mixture Models (GMM\u27s) can be utilized to effectively model the distribution of feature vectors present in speech signals for classification. Common feature vectors used for speech processing include Linear Prediction (LP-CC), Mel-Frequency (MF-CC), and Perceptual Linear Prediction derived Cepstral coefficients (PLP-CC). This thesis compares and examines the recently proposed type of feature vector called the Shifted Delta Cepstral (SDC) coefficients. Utilization of the Shifted Delta Cepstral coefficients has been shown to improve language identification performance. This thesis explores the use of different types of shifted delta cepstral feature vectors for spoken language identification of telephone speech using a simple Gaussian Mixture Models based classifier for a 3-language task. The OGI Multi-language Telephone Speech Corpus is used to evaluate the system