Speaker Recognition

We present a method for speaker recognition that uses the duration patterns of speech units to aid speaker classification. The approach represents each word and/or phone by a feature vector comprised of either the durations of the individual phones making up the word, or the HMM states making up the phone. We model the vectors using mixtures of Gaussians. The speaker specific models are obtained through adaptation of a "background" model that is trained on a large pool of speakers. Speaker models are then used to score the test data; they are normalized by subtracting the scores obtained with the background model. We find that this approach yields significant perfomance improvement when combined with a state-of-the-art speaker recognition system based on standard cepstral features. Furthermore, the improvement persists even after combination with lexical features. Finally, the improvement continues to increase with longer test sample durations, beyond the test duration at which standard system accuracy level off.

This paper presents the deep neural networks to classification of children with voice impairments from speech signals. In the analysis of speech signals, 6,373 static acoustic features are extracted from many kinds of low-level-descriptors and functionals. To reduce the variability of extracted features, two-dimensional normalizations are applied to smooth the interspeaker and inter-feature mismatch using the feature warping approach. Then, the feature selection is used to explore the discriminative and low-dimensional representation based on techniques of principal component analysis and linear discriminant analysis. In such representation, the robust features are obtained by eliminating noise features via subspace projection. Finally, the deep neural networks are adopted to classify the children with voice impairments. We conclude that deep neural networks with the proposed feature normalization and selection can significantly contribute to the robustness of recognition in practical application scenarios. We have achieved an UAR of 60.9% for the four-way diagnosis classification on the development set. This is a relative improvement of 16.2% to the official baseline by using our single system. I.

Most elderly people monitoring systems include the detection of abnormal situations, in particular distress situations, as one of their main goals. In order to reach this objective, many solutions end up combining several modalities such as video tracking, fall detection and sound recognition, so as to increase the reliability of the system. In this work we focus on daily sound recognition as it is one of the most promising modalities. We make a comparison of two standard methods used for speaker recognition and verification: Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). Experimental results show the effectiveness of the combination of GMM and SVM in order to classify sound data sequences when compared to systems based on GMM.

The paper describes a multisensorial personidentification system: visual and acoustic cues are usedjointly for person identification. A simple approach,based on the fusion of the lists of scores produced independentlyby a speaker recognition system and a facerecognition system, is presented. Experiments are reportedwhich show that integration of visual and acousticinformation enhances both performance and reliabilityof the separate systems. Finally two network

Recently satisfactory results have been obtained in NIST speaker recognition evaluations. These results are mainly due to accurate modeling of a very large development dataset provided by LDC. However, for many realistic scenarios the use of this development dataset is limited due to a dataset mismatch. In such cases, collection of a large enough dataset is infeasible. In this work we analyze the sources of degradation for a particular setup in the context of an i-vector PLDA system and conclude that the main source for degradation is an i-vector dataset shift. As a remedy, we introduce inter dataset variability compensation (IDVC) to explicitly compensate for dataset shift in the i-vector space. This is done using the nuisance attribute projection (NAP) method. Using IDVC we managed to reduce error dramatically by more than 50% for the domain mismatch setup

— An automatic verification of person's identity from its voice is a part of modern telecommunication services. In order to execute a verification task, a speech signal has to be transmitted to a remote server. So, a performance of the verification system can be influenced by various distortions that can occur when transmitting a speech signal through a communication channel. This paper studies an effect of the state of art wideband (WB) speech codecs on a performance of automatic speaker verification in the context of a channel/codec mismatch between enrollment and test utterances. The speaker verification system is developed on GMM-UBM method. The results show that EVS codec provides the best performance over all the investigated scenarios in this study. Moreover, deploying G.729.1 codec in a training process of the verification system provides the best equal error rate in the fully-codec mismatched scenario. Anyhow, differences between the equal error rates reported for all of the codecs involved in this scenario are mostly nonsignificant.

This paper describes a new identity authentication technique by a synergetic use of lip-motion and speech. The lip-motion is defined as the distribution of apparent velocities in the movement of brightness patterns in an image and is estimated by computing the velocity components of the structure tensor by 1D processing, in 2D manifolds. Since the velocities are computed without extracting the speaker's lip-contours, more robust visual features can be obtained in comparison to motion features extracted from lip-contours. The motion estimations are performed in a rectangular lip-region, which affords increased computational efficiency. A person authentication implementation based on lip-movements and speech is presented along with experiments exhibiting a recognition rate of 98%. Besides its value in authentication, the technique can be used naturally to evaluate the ''liveness'' of someone speaking as it can be used in text-prompted dialogue. The XM2VTS database was used for performance quantification as it is currently the largest publicly available database (%300 persons) containing both lip-motion and speech. Comparisons with other techniques are presented.

One characteristic that distinguishes speaker recognition (identification, verification, classification, tracking, etc.) from other biometrics is that it is designed to operate with devices and over channels that were created for other technologies and functions. That characteristic supports broad, inexpensive, and speedy deployments. The explosion of mobile devices has exacerbated the mismatch problem and the challenges for interoperability. This paper presents a detailed proposal for interoperability that supports all types of audio interchange operations while, at the same time, limiting the audio formats to a small set of widely-used, open standards. We call this proposal Standard Audio Format Encapsulation (SAFE). The SAFE proposal has been incorporated into speaker-recognition data interchange draft standards by the M1 (biometrics) committee of ANSI/INCITS and ISO/IEC JTC1/SC37 project 19794-13 (Voice data).

In the meeting case scenario, audio is often recorded using Multiple Distance Microphones (MDM) in a non-intrusive manner. Typically a beamforming is performed in order to obtain a single enhanced signal out of the multiple channels. This paper investigate s the use of mutual information for selecting the channel subset that produces the lowest error in a diarization system. Conventional

Il progetto verte sullo sviluppo di un sistema di riconoscimento del parlatore per l'esecuzione di comandi vocali. Il sistema è stato implementato in Python e si occupa del riconoscimento sia del linguaggio parlato che del parlatore. Per fare questo è stata utilizzata una rete neurale profonda (DNN) e un classificatore Multi Layer Perceptron.
I risultati sono ottimi, il sistema oltre che a discriminare sempre un utente dall'altro, dispone di un ulteriore modulo che si occupa della traduzione in testo del comando impartito e un preventivo salvataggio su file di testo, per una possibile condivisione futura. Questo permette diverse implementazioni sfruttando la rete aziendale, caricando tutto il sistema su Cloud, interfacciando così l'utente a tutte quelle tecnologie dell'internet presenti negli Smart Office moderni. In secondo luogo è stato sviluppato anche un sistema di Sentiment Analysis utilizzando la voce, che completa il lavoro svolto e potrebbe monitorare l'umore dei dipendenti in determinate aree.

Speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. Voice -recognition is combination of the two where it uses learned aspects of a speaker’s voice to determine what is being said - such a system cannot recognize speech from random speakers very accurately, but it can reach high accuracy for individual voices it has been trained with, which gives us various applications in day today life.

Identification of non-native personnel is a critical piece of information for making crucial on-the-spot decisions for security purposes. Identification of a non-native speaker is often readily apparent in normal conversation with a native speaker through speech content and accent. Such identification which requires familiarity with language nuances may not be possible for a non-native interrogator or intelligence analyst or when conversing or listening through a machine language translator. Developing an automatic system to identify speakers as native or non-native, as well as their native language, including dialect, within input audio streams, is the major goal of this project. Such a system may be used alone or with other downstream applications such as machine language translation systems. In this paper we present four approaches to identify native and nonnative speakers as a binary recognition problem. The approaches can be further categorized into phonetic-based approaches and non-phonetic-based approaches. These approaches were tested on two separate databases, including text-dependent read speech and text-independent spontaneous speech. The results show that our system is competitive in comparison with other published, state-of-the-art non-native speaker recognition systems. Key metrics for automated non-native recognition systems include: 1) positive identification rates, 2) false alarm/identification rates, and 3) length of captured speech sample required to reach a decision.

Device, language and environmental mismatch adversely affect speaker verification (SV) performance. We investigate such effects empirically based on the M3 (multibiometric, multilingual and multi-device) Corpus [1]. Device mismatch (among 3G phone, PocketPC and a desktop PC plug-in microphone) brings relative performance degradation of 523%; language mismatch (between English and Cantonese) brings 284% and environmental mismatch (between office environment and recording studio) brings 109%. In particular, verification with wide-band models on narrow-band test data outperforms narrow-band models on wide-band test data. The 3G phone's SV performance is generally low, but remains stable across environments. Additionally, durational variations within two-second utterances may cause a relative change of 633% in SV performance.

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported.

Availability of databases is a necessity in the speech processing field. The publically available databases in Arabic language are few. In this paper we describe a rich database for Arabic language. The database is rich in many dimensions: in text, environments, microphone type, number of recording sessions, recording system, the transmission channel, the country of origin, and the mother language. This richness makes the database an important resource for research in Arabic Language processing and very useful in many speech processing tasks, such as speaker recognition, speech recognition, and accent identification. The speakers were speaking in Modern Standard Arabic (MSA).

Feeling of knowing (or expressed confidence) reflects a speaker's certainty or commitment to a statement and can be associated with one's trustworthiness or persuasiveness in social interaction. We investigated the perceptual-acoustic correlates of expressed confidence and doubt in spoken language, with a focus on both linguistic and vocal speech cues. In Experiment 1, utterances subserving different communicative functions (e.g., stating facts, making judgments) produced in a confident, close-to-confident, unconfident, and neutral-intending voice by six speakers, were then rated for perceived confidence by 72 native listeners. As expected, speaker confidence ratings increased with the intended level of expressed confidence; neutral-intending statements were frequently judged as relatively high in confidence. The communicative function of the statement, and the presence vs. absence of an utterance-initial probability phrase (e.g., Maybe, I'm sure), further modulated speaker confidence ratings. In Experiment 2, acoustic analysis of perceptually valid tokens rated in Experiment 1 revealed distinct patterns of pitch, intensity and temporal features according to perceived confidence levels; confident expressions were highest in fundamental frequency (f0) range, mean amplitude, and amplitude range, whereas unconfident expressions were highest in mean f0, slowest in speaking rate, with more frequent pauses. Dynamic analyses of f0 and intensity changes across the utterance uncovered distinctive patterns in expression as a function of confidence level at different positions of the utterance. Our findings provide new information on how metacognitive states such as confidence and doubt are communicated by vocal and linguistic cues which permit listeners to arrive at graded impressions of a speaker's feeling of (un)knowing.

Pre-processing of Speech Signal serves various purposes in any speech processing application. It includes Noise Removal, Endpoint Detection, Pre-emphasis, Framing, Windowing, Echo Canceling etc. Out of these, silence/unvoiced portion removal along with endpoint detection is the fundamental step for applications like Speech and Speaker Recognition. The proposed method uses Probability Density Function (PDF) of the background noise and a Linear Pattern Classifier for classification of Voiced part of a speech from silence/unvoiced part. The work shows better end point detection as well as silence removal than conventional Zero Crossing Rate (ZCR) and Short Time Energy (STE) function methods.

Reliable identity management must be built with an accurate user identity recognition method. This recognition usually is the core of the authentication method which is the essential part of any identity management system. The authentication must carefully be designed, especially when it is used among different service providers. The authentication is a user identity verifying and protecting mechanism. It consists of three main components, user identity attributes, the verification method, and the log-in mechanism. The log-in component has great impact on authentication, when the user needs to be authenticated to be given access to several service providers. In addition, the verification of the claimed user attributes, involve the decisive role in the authentication because it will produce the final decision of the identity proving process, so it is important to be accurate and intelligent as much as possible. In this paper, a central intelligent biometric authentication approach is proposed; this authentication is based on the Mel-frequency Cepstral coefficients (MFCC) voice attributes, fuzzy classifier, and client-server model as log-in mechanism. The proposed fuzzy classifier depends on the fuzzy set inner product and a predefined threshold. This classifier is designed as an intelligent identity verification method. The experiments show 95.45% accuracy in offline user authentication using ELSDSR dataset.

It's me!" This pronouncement is usually made over the telephone or at an entryway out of sight of the intended hearer. It embodies the expectation that the sound of one's voice is sufficient for the hearer to recognize the speaker. In short, "It's me!" is the original real-world, speaker-recognition challenge. Like human listeners, voice biometrics uses the features of a person's voice to ascertain the speaker's identity. The best-known commercialized forms of voice biometrics are Speaker Recognition. Speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. Voice signal carries information related to not only the message to be conveyed , but also about speaker , language, emotional status of the speaker, environment and so on. In a speaker recognition task the speech signal is processed to extract speakerspecific information. Speaker recognition system (SRS) is one of the most viable biometric authentication systems. In this paper we have presented a literature survey on SRS. This literature survey paper gives brief introduction on SRS first, and then discusses general architecture of SRS, biometric standards relevant to voice/speech, then existing commercial application of SRS, its market survey, national and international status. We have also surveyed various approaches for SRS. Finally, on the basis of our literature survey we conclude by providing the best method and approach for speaker recognition system. biometrics requires proprietary hardware, such as the vendor's fingerprint sensor or iris-scanning equipment. By definition, voice biometrics is always linked to a particular speaker. The best-known commercialized forms of voice biometrics are Speaker Recognition. Speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices.

Recent studies show that Gaussian mixture model (GMM) weights carry less, yet complementary, information to GMM means for language and dialect recognition. However, state-of-the-art language recognition systems usually do not use this information. In this research, a non-negative factor analysis (NFA) approach is developed for GMM weight decomposition and adaptation. This modeling, which is conceptually simple and computationally inexpensive, suggests a new low-dimensional utterance representation method using a factor analysis similar to that of the i-vector framework. The obtained subspace vectors are then applied in conjunction with i-vectors to the language/dialect recognition problem. The suggested approach is evaluated on the NIST 2011 and RATS language recognition evaluation (LRE) corpora and on the QCRI Arabic dialect recognition evaluation (DRE) corpus. The assessment results show that the proposed adaptation method yields more accurate recognition results compared to three conventional weight adaptation approaches, namely maximum likelihood re-estimation, non-negative matrix factorization, and a subspace multinomial model. Experimental results also show The works of M.H. DRAFT 2 that the intermediate-level fusion of i-vectors and NFA subspace vectors improves the performance of the state-of-the-art i-vector framework especially for the case of short utterances. I. INTRODUCTION Language and dialect/accent recognition has received increased attention during the recent decades due to its importance for the enhancement of automatic speech recognition (ASR) [1], [2], multi-language translation systems, service customization, targeted advertising, and forensics softwares [3], [4].

In this study, we present a binaural scene analyzer that is able to simultaneously localize, detect and identify a known number of target speakers in the presence of spatially positioned noise sources and reverberation. In contrast to many other binaural cocktail party processors, the proposed system does not require a priori knowledge about the azimuth position of the target speakers. The proposed system consists of three main building blocks: binaural localization, speech source detection, and automatic speaker identification. First, a binaural front-end is used to robustly localize relevant sound source activity. Second, a speech detection module based on missing data classification is employed to determine whether detected sound source activity corresponds to a speaker or to an interfering noise source using a binary mask that is based on spatial evidence supplied by the binaural front-end. Third, a second missing data classifier is used to recognize the speaker identities of all detected speech sources. The proposed system is systematically evaluated in simulated adverse acoustic scenarios. Compared to state-of-the art MFCC recognizers, the proposed model achieves significant speaker recognition accuracy improvements.

Speaker recognition is the process of recognizing a
speaker’s identity by his or her voice. Humans sound differently
and there are features in our speaking voice which differentiate
us from other people. In this paper, we show an end-to-end
implementation of a Speaker Recognition System. One of the
most popular features used in Speaker Recognition which is
based on the human Auditory System is the Mel Frequency
Cepstral Coefficients (MFCC). MFCC yields high performing
results however is quite sensitive to additive noise distortion.
When supplemented with feature robust to noise such as the
Spectral Subband Centroids (SSC), speaker recognition performance
increases. We also show an array of binary SVM per
speaker outperforms other out-of-the-box multi-class classifiers
in determining the speaker in audio frames.

This paper presents the feature analysis and design of compensators for speaker recognition under stressed speech conditions. Any condition that causes a speaker to vary his or her speech production from normal or neutral condition is called stressed speech condition. Stressed speech is induced by emotion, high workload, sleep deprivation, frustration and environmental noise. In stressed condition, the characteristics of speech signal are different from that of normal or neutral condition. Due to changes in speech signal characteristics, performance of the speaker recognition system may degrade under stressed speech conditions. Firstly, six speech features (mel-frequency cepstral coefficients (MFCC), linear prediction (LP) coefficients, linear prediction cepstral coefficients (LPCC), reflection coefficients (RC), arc-sin reflection coefficients (ARC) and log-area ratios (LAR)), which are widely used for speaker recognition, are analyzed for evaluation of their characteristics under stressed condition. Secondly, Vector Quantization (VQ) classifier and Gaussian Mixture Model (GMM) are used to evaluate speaker recognition results with different speech features. This analysis help select the best feature set for speaker recognition under stressed condition. Finally, four VQ based novel compensation techniques are proposed and evaluated for improvement of speaker recognition under stressed condition. The compensation techniques are speaker and stressed information based compensation (SSIC), compensation by removal of stressed vectors G. Senthil Raja ( ) iKoa semiconductor pvt.

Standard speaker recognition system employs a pre-processed form of an acoustic signal, which provides information about the distribution of signal energy across time and frequency. However, different signal representations may be employed, either as genuine alternatives to the acoustic representation, or as additional sources of information. Voice articulator encourages a viability and a potential of the speech signal representation especially in a Thai speaker recognition system. Applying the biometrical voice articulator additionally with a Backpropagation multilayered perceptron attains a high recognition accuracy. LPC and MFCC with several coefficient orders have been performed comparatively. The highest percentage of recognition accuracy with an efficient computational time is 97 .24% belonging to the Bilabial articulator from the l6'n coefficient order of MFCC. 1. Introduction Speaker recognition in a recognition area of speech processing, is one of be biometric identific...

Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.

Dedicated to my parents who sacrificed their today for our better tomorrow and my mentors who have guided me throughout my research and helped me to improve professionally and personally. ACKNOWLEDGMENTS I would like to gratefully acknowledge the guidance and support of my thesis advisor, Dr. Ravi Sankar, whose insightful comments and explanations have taught me a great deal about speech and research in general. I am also grateful to Dr. Wilfrido Moreno and Dr. Paris Wiley for serving on my committee. I would also like to thank iCONS group members, especially Tanmoy Islam for their valuable comments on this work. Finally I would like to thank my mother Chandrabali my father Basab and my sister Bindi, for their encouragement, support and love.

A simple yet complex approach to modern sophistication.
In this project we used the MFCC approach to build a unique and accurate coefficients extracting processor to extract feature from the voice stored in the database, then on the next stage we again emoployed the use of MFCC to extract coefficients and used vector quantization to match with the user database. For vector Quantization we used Linde-Buzo Grey Algorithm..
Code is available on demand. Send me a private message if you want it.

The paper discusses Derrida's concept of hospitality which perfectly describes the experience of loosing the sense of feeling at home and reveals the disintegrating entrance of the Otherness into a coherent home space. Jacques Derrida's theory makes it possible to deconstruct the familiar home space, which, however, does not constitute in any case its destruction. Revealing a complex and ambiguous nature of the challenge of hospitality, he perceives the sense of feeling at home as one of the necessary conditions of a stable subject. This is the subject that most fears the Otherness which could disrupt, unsettle their permanent structure and a sense of certainty. Therefore, the Author follows closely the fear of strangeness and the nostalgic longing for the sense of feeling at home as depicted by David Lynch and Michael Haneke in their films. Then he passes on to the field of contemporary art which not only reveals the fact that something has happened to the house we have known so far, but it also allows for creative deconstruction of the feeling at home. The artistic works selected by the Author break the binary oppositions: one’s own/someone else’s, familiar/strange (Jessica Sue Layton), close/distant (Shizuka Yokomizo) and finally the most important one: interior/exterior (Marja Pirilä, Eve Dent).

ABSTRACT:
Defining the vowel system in comparison with the previous similar works for standard Turkish by using a wider database and determining the rate of speaker specific invariance are two aims of this study.
In the previous similar studies on standard Turkish regarding the determination of vowel system based on fundamental frequency and formants, it is notable that focus is on male speakers, the number of speakers is approximately 10 or 15 and generally only the first three formants are analysed. In the current study, fundamental frequency and first four formants of speech from 40 male and female speakers were analyzed. Minimum and maximum values, averages and standard deviations were calculated and compared with the previous studies. Besides, wovel quadrilateral that is used to show approximate positions of vowels inside the mouth during an utterance was produced based on the calculations mentioned above for male and female speakers, in an F1-F2 scatter plot.
To determine which variables are speaker specific to what extent, frequency values, skewness/kurtosis values that are calculated from statistical distribution of these frequency values and ratio of energy levels in different frequency levels during the utterance of a vowel were used as variables in this study. It is shown that, of all these variables, only the frequency values as a whole give statistically significant results and carry speaker specific information. As a result of this study, it is also shown that which of these frequency values are speaker specific to what extent and for what type of situations.

OZET:
Daha geniş bir veri tabanı kullanılarak ölçünlü Türkçe için ünlü dizgesinin daha önceki çalışmalarla karşılaştırmalı olarak saptanması ve konuşucuya özgü değişmezliğin oranının ortaya konması bu çalışmanın iki amacını meydana getirmektedir.
Ünlülerin temel frekans ve formant frekans değerlerine dayalı olarak saptanması konusunda ölçünlü Türkçe üzerine yapılan çalışmalara bakıldığında daha çok erkek konuşucular üzerinde yoğunlaşıldığı, konuşucu sayısının 10-15 civarında olduğu ve genellikle ilk üç formant frekansının çalışma kapsamına alındığı görülmektedir. Bu çalışmada 40 erkek ve 40 kadın konuşucuya ait sesler üzerinde inceleme yapılmış ve temel frekans ile ilk dört formant frekansı üzerinde inceleme yapılmıştır. İnceleme neticesinde ünlüler için en küçük ve en büyük değerler, ortalamalar ve standart sapmalar hesaplanmış ve diğer çalışmalardaki değerlerle karşılaştırılmıştır. Ayrıca elde edilen hesaplamalar kullanılarak kabaca ünlülerin ağız içindeki çıkış yerlerini gösteren ünlü dörtgeni, yapılmış olan ölçümlere dayalı olarak bir grafik üzerinde, erkek ve kadın konuşucular için oluşturulmuştur.
Hangi değişkenlerin ne ölçüde konuşucuya özgü olduğunun saptanması için frekans değerleri, bu değerlerin istatistiksel dağılımına dayalı olarak hesaplanan çarpıklık/basıklık değerleri ve ünlülerin sesletimi esnasında değişik frekans seviyelerindeki enerjilerin birbirine oranları değişkenler olarak kullanılmıştır. Bütün bu değişkenler içinde sadece frekans değerlerinin tamamının istatistiksel olarak anlamlı sonuçlar verdiği ve kişiye özgülük taşıdığı saptanmıştır. Çalışma sonunda bu frekans değerleri içinde hangilerinin, hangi durumlarda, ne oranda konuşucuya özgülük taşıdıkları ortaya konmuştur.

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on different feature extraction techniques. Our experimental results assessed the robustness of the system in front changes on time (different sessions) and robustness in front of changes of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationally with the number of scores to be fusioned as the simplex method for linear programming.

A wide variety of systems require reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user, and not anyone else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or simply biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). In this paper, we give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

This paper aims at inscribing Forensic Linguistics within the variegate field of Forensic sciences. After a deep and meticulous description of the state of art of Forensic Linguistics from 1960s until now, we propose all of the methodologies, concerning both the written and the oral facies of this linguistic discipline applied to Forensic field, and then we focuse on methods used in transcriptions and in Speaker and Speech Recognition analyses. Then, the discussion outlines the importance for Forensic Linguistics to be considered as a proper scientific discipline: in this way, its methods and its results could be tested and replied by the scientific community, by verifying the uncontested principle of scientific cumulability. Our hope is that academic and training courses ad hoc as regards Forensic Linguistics could be established as soon as possible and that this sphere of research will be handled with an interdisciplinary approach. We think that interdisciplinary competences are necessary to deal with a huge discipline, such as Forensic Linguistics and to conduct linguistic research (e.g. Forensic transcriptions, Speaker and Speech Recognition) in the best way.

Superbisor sa Programang Edukasyon sa Filipino at Mother Tongue Based-Multi Lingual Education ng Kagawaran ng Edukasyon, Lungsod ng Maynila. DepEd Concept Paper Writer, Translator, Editor and National Lead Trainer. International Book Translator of E-Book. Siya rin ang kasalukuyang Transformational President ng Rotary Club of Manila, Maynilad Seafrot District 3810 at ang Pangulo ng Samahan ng mga Superbisor sa NCR-Filipino at nahirang na Pambansang Lakan ng mga Superbisor sa Buong Bansa.

Tracking speakers in multiparty conversations constitutes a fundamental task for automatic meeting analysis. In this paper, we present a novel probabilistic approach to jointly track the location and speaking activity of multiple speakers in a multisensor meeting room, equipped with a small microphone array and multiple uncalibrated cameras. Our framework is based on a mixed-state dynamic graphical model defined on a multiperson state-space, which includes the explicit definition of a proximity-based interaction model.