A False Acceptance Error Controlling Method for Hyperspherical Classifiers

Controlling false acceptance errors is of critical importance in many pattern recognition applications, including signature and speaker verification problems. Toward this goal, this paper presents two post-processing methods to improve the performance of hyperspherical classifiers in rejecting patterns from unknown classes. The first method uses a self-organizational approach to design minimum radius hyperspheres, reducing the redundancy of the class region defined by the hyperspherical classifiers. The second method removes additional redundant class regions from the hyperspheres by using a clustering technique to generate a number of smaller hyperspheres. Simulation and experimental results demonstrate that by removing redundant regions these two post-processing methods can reduce the false acceptance error without significantly increasing the false rejection error

Automatic speaker segmentation using multiple features and distance measures: a comparison of three approaches

This paper addresses the problem of unsupervised speaker change detection. Three systems based on the Bayesian Information Criterion (BIC) are tested. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic thresholding followed by a fusion scheme, and finally applies BIC. The second method is a real-time one that uses a metric-based approach employing the line spectral pairs and the BIC to validate a potential speaker change point. The third method consists of three modules. In the first module, a measure based on second-order statistics is used; in the second module, the Euclidean distance and T2 Hotelling statistic are applied; and in the third module, the BIC is utilized. The experiments are carried out on a dataset created by concatenating speakers from the TIMIT database, that is referred to as the TIMIT data set. A comparison between the performance of the three systems is made based on t-statistics

A novel text-independent speaker verification method based on the global speaker model

2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Integration of speech biometrics in a phone payment system: text-independent speaker verification

Integration of a speaker recognition system in a payment system by phone.Nowadays, the integration of biometrics in security systems is a prominent research and application field. Also, it is clear that speech is the most common form of communication, which makes a swell candidate. While using speech as a biometric, one could say there are two types of systems that should be analyzed: those systems which do know what the speaker is going to say upon verification and those that do not. This degree thesis offers an overview of both systems, focusing on those that do not know what the speaker is going to say beforehand, also known as textindependent systems. To be able to determine which would be the best approach to integrate speech biometrics into a security system, both types of systems are compared; and two methodologies are also analyzed for the text-independent system. To conclude, one of those methodologies is implemented in a software library which allows the creation a text-independent speaker verification system.En l’actualitat, la integració de biometries en els sistemes de seguretat és una branca d’investigació i aplicacions prominent. A més a més, la veu és un dels mitjans més comuns de comunicació, cosa que fa que sigui una bona candidata per a aquests sistemes. Si prenem la parla com a biometria, es pot dir que hi ha dos tipus de sistemes bastant diferenciats a analitzar: aquells sistemes els quals saben el que dirà la persona que s’intenta verificar i aquells que no saben el que dirà. Aquest treball ofereix una visió àmplia dels dos tipus de sistemes, centrant-se en els sistemes on no es sap el que es dirà, també coneguts com sistemes de text independent. Per decidir quin seria la millor manera d’integrar la parla com a biometria en un sistema de seguretat, es comparen ambdós sistemes i, en el cas del sistema de text independent, es comparen també dues metodologies diferents. Per acabar, s’implementa una d’aquestes metodologies a unes llibreries de software per dur a terme un sistema de verificació de locutor amb text independent.En la actualidad, la integración de biometrías en los sistemas de seguridad es una rama de investigación y de aplicaciones prominente. Además, está claro que la voz es el medio más común de comunicación y es por eso que es una buena candidata. Usando el habla como biometría, se podría decir que hay dos tipos de sistemas diferentes a analizar: aquellos sistemas que saben de antemano aquello que va a decir el locutor que intenta verificarse y aquellos que no lo saben. Este trabajo ofrece una visión amplia de los dos tipos de sistemas, centrándose en los sistemas donde aquello que se va a decir no se sabe, también conocidos como sistemas de texto independiente. Para decir cuál sería la mejor manera de integrar el habla como biometría en un sistema de seguridad se comparan ambos sistemas y, en el caso del sistema de texto independiente, se comparan también dos metodologías diferentes. Para finalizar, se implementa una de estas últimas en unas librerías de software para poder llevar a cabo un sistema de verificación de locutor de texto independiente

Speaker Identification Based On Discriminative Vector Quantization And Data Fusion

Speaker Identification (SI) approaches based on discriminative Vector Quantization (VQ) and data fusion techniques are presented in this dissertation. The SI approaches based on Discriminative VQ (DVQ) proposed in this dissertation are the DVQ for SI (DVQSI), the DVQSI with Unique speech feature vector space segmentation for each speaker pair (DVQSI-U), and the Adaptive DVQSI (ADVQSI) methods. The difference of the probability distributions of the speech feature vector sets from various speakers (or speaker groups) is called the interspeaker variation between speakers (or speaker groups). The interspeaker variation is the measure of template differences between speakers (or speaker groups). All DVQ based techniques presented in this contribution take advantage of the interspeaker variation, which are not exploited in the previous proposed techniques by others that employ traditional VQ for SI (VQSI). All DVQ based techniques have two modes, the training mode and the testing mode. In the training mode, the speech feature vector space is first divided into a number of subspaces based on the interspeaker variations. Then, a discriminative weight is calculated for each subspace of each speaker or speaker pair in the SI group based on the interspeaker variation. The subspaces with higher interspeaker variations play more important roles in SI than the ones with lower interspeaker variations by assigning larger discriminative weights. In the testing mode, discriminative weighted average VQ distortions instead of equally weighted average VQ distortions are used to make the SI decision. The DVQ based techniques lead to higher SI accuracies than VQSI. DVQSI and DVQSI-U techniques consider the interspeaker variation for each speaker pair in the SI group. In DVQSI, speech feature vector space segmentations for all the speaker pairs are exactly the same. However, each speaker pair of DVQSI-U is treated individually in the speech feature vector space segmentation. In both DVQSI and DVQSI-U, the discriminative weights for each speaker pair are calculated by trial and error. The SI accuracies of DVQSI-U are higher than those of DVQSI at the price of much higher computational burden. ADVQSI explores the interspeaker variation between each speaker and all speakers in the SI group. In contrast with DVQSI and DVQSI-U, in ADVQSI, the feature vector space segmentation is for each speaker instead of each speaker pair based on the interspeaker variation between each speaker and all the speakers in the SI group. Also, adaptive techniques are used in the discriminative weights computation for each speaker in ADVQSI. The SI accuracies employing ADVQSI and DVQSI-U are comparable. However, the computational complexity of ADVQSI is much less than that of DVQSI-U. Also, a novel algorithm to convert the raw distortion outputs of template-based SI classifiers into compatible probability measures is proposed in this dissertation. After this conversion, data fusion techniques at the measurement level can be applied to SI. In the proposed technique, stochastic models of the distortion outputs are estimated. Then, the posteriori probabilities of the unknown utterance belonging to each speaker are calculated. Compatible probability measures are assigned based on the posteriori probabilities. The proposed technique leads to better SI performance at the measurement level than existing approaches