The number of biometric applications has increased a lot in the last few years. In this context, the automatic person recognition by some physical traits like fingerprints, face, voice or iris, plays an important role. Users demand this type of applications every time more and the technology seems already mature. People look for security, low cost and accuracy but, at the same time, there are many other factors in connection with biometric applications that are growing in importance. Intrusiveness is undoubtedly a burning factor to decide about the biometrics we will used for our application. At this point, one can realize about the suitability of speaker recognition because voice is the natural way of communicating, can be remotely used and provides a low cost. Automatic speaker recognition is commonly used in telephonic applications although it can also be used in ...

Speech signals carry important information about a speaker such as age, gender, language, accent and emotional/psychological state. Automatic recognition of speaker characteristics has a wide range of commercial, medical and forensic applications such as interactive voice response systems, service customization, natural human-machine interaction, recognizing the type of pathology of speakers, and directing the forensic investigation process. This research aims to develop accurate methods and tools to identify different physical characteristics of the speakers. Due to the lack of required databases, among all characteristics of speakers, our experiments cover gender recognition, age estimation, language recognition and accent/dialect identification. However, similar approaches and techniques can be applied to identify other characteristics such as emotional/psychological state. For speaker characterization, we first convert variable-duration speech signals into fixed-dimensional vectors suitable for classification/regression algorithms. This is performed by fitting a probability density function to acoustic ...

When exposed to noise, speakers will modify the way they speak in an effort to maintain intelligible communication. This process, which is referred to as Lombard effect (LE), involves a combination of both conscious and subconscious articulatory adjustment. Speech production variations due to LE can cause considerable degradation in automatic speech recognition (ASR) since they introduce a mismatch between parameters of the speech to be recognized and the ASR system’s acoustic models, which are usually trained on neutral speech. The main objective of this thesis is to analyze the impact of LE on speech production and to propose methods that increase ASR system performance in LE. All presented experiments were conducted on the Czech spoken language, yet, the proposed concepts are assumed applicable to other languages. The first part of the thesis focuses on the design and acquisition of a ...

The subject of this PhD thesis is the efficient and robust processing and analysis of the audio recordings that are derived from a call center. The thesis is comprised of two parts. The first part is dedicated to dialogue/non-dialogue detection and to speaker segmentation. The systems that are developed are prerequisite for detecting (i) the audio segments that actually contain a dialogue between the system and the call center customer and (ii) the change points between the system and the customer. This way the volume of the audio recordings that need to be processed is significantly reduced, while the system is automated. To detect the presence of a dialogue several systems are developed. This is the first effort found in the international literature that the audio channel is exclusively exploited. Also, it is the first time that the speaker utterance ...

Nowadays, speech synthesis is part of various daily life applications. The ultimate goal of such technologies consists in extending the possibilities of interaction with the machine, in order to get closer to human-like communications. However, current state-of-the-art systems often lack of realism: although high-quality speech synthesis can be produced by many researchers and companies around the world, synthetic voices are generally perceived as hyperarticulated. In any case, their degree of articulation is fixed once and for all. The present thesis falls within the more general quest for enriching expressivity in speech synthesis. The main idea consists in improving statistical parametric speech synthesis, whose most famous example is Hidden Markov Model (HMM) based speech synthesis, by introducing a control of the articulation degree, so as to enable synthesizers to automatically adapt their way of speaking to the contextual situation, like humans ...

This Thesis is focused on the combination of multiple biometric traits for automatic person authentication, in what is called a multimodal biometric system. More generally, any type of biometric information can be combined in what is called a multibiometric system. The information sources in multibiometrics include not only multiple biometric traits but also multiple sensors, multiple biometric instances (e.g., different fingers in fingerprint verification), repeated instances, and multiple algorithms. Most of the approaches found in the literature for combining these various information sources are based on the combination of the matching scores provided by individual systems built on the different biometric evidences. The combination schemes following this architecture are typically based on combination rules or trained pattern classifiers, and most of them assume that the score level fusion function is fixed at verification time. This Thesis considers the problem of ...

The process of ageing causes changes to the voice over time. There have been significant research efforts in the automatic speaker recognition community towards improving performance in the presence of everyday variability. The influence of long-term variability, due to vocal ageing, has received only marginal attention however. In this Thesis, the impact of vocal ageing on speaker verification and forensic speaker recognition is assessed, and novel methods are proposed to counteract its effect. The Trinity College Dublin Speaker Ageing (TCDSA) database, compiled for this study, is first introduced. Containing 26 speakers, with recordings spanning an age difference of between 28 and 58 years per speaker, it is the largest longitudinal speech database in the public domain. A Gaussian Mixture Model-Universal Background Model (GMM-UBM) speaker verification experiment demonstrates a progressive decline in the scores of genuine-speakers as the age difference between ...

It takes more time to think of a silent scene, action or event than finding one that emanates sound. Not only speaking or playing music but almost everything that happens is accompanied with or results in one or more sounds mixed together. This makes acoustic event detection (AED) one of the most researched topics in audio signal processing nowadays and it will probably not see a decline anywhere in the near future. This is due to the thirst for understanding and digitally abstracting more and more events in life via the enormous amount of recorded audio through thousands of applications in our daily routine. But it is also a result of two intrinsic properties of audio: it doesn’t need a direct sight to be perceived and is less intrusive to record when compared to image or video. Many applications such ...

This Thesis is focused on the use of automatic speaker recognition systems for forensic identification, in what is called forensic automatic speaker recognition. More generally, forensic identification aims at individualization, defined as the certainty of distinguishing an object or person from any other in a given population. This objective is followed by the analysis of the forensic evidence, understood as the comparison between two samples of material, such as glass, blood, speech, etc. An automatic speaker recognition system can be used in order to perform such comparison between some recovered speech material of questioned origin (e.g., an incriminating wire-tapping) and some control speech material coming from a suspect (e.g., recordings acquired in police facilities). However, the evaluation of such evidence is not a trivial issue at all. In fact, the debate about the presentation of forensic evidence in a court ...

The narrowband frequency range of telephone speech signals originally caused by former analog transmission techniques still leads to frequent acoustical limitations in today’s digital telephony systems. It provokes muffled sounding phone calls with reduced speech intelligibility and quality. By means of artificial speech bandwidth extension approaches, missing frequency components can be estimated and reconstructed. However, the artificially extended speech bandwidth typically suffers from annoying artifacts. Particularly susceptible to this are the fricatives /s/ and /z/. They can hardly be estimated based on the narrowband spectrum and are therefore easily confusable with other phonemes as well as speech pauses. This work takes advantage of phonetic a priori knowledge to optimize the performance of artificial bandwidth extension. Both the offline training part conducted in advance and the main processing part performed later on shall be thereby provided with important phoneme information. As ...

Con dence measures for the results of speech/speaker recognition make the systems more useful in the real time applications. Con dence measures provide a test statistic for accepting or rejecting the recognition hypothesis of the speech/speaker recognition system. Speech/speaker recognition systems are usually based on statistical modeling techniques. In this thesis we de ned con dence measures for statistical modeling techniques used in speech/speaker recognition systems. For speech recognition we tested available con dence measures and the newly de ned acoustic prior information based con dence measure in two di erent conditions which cause errors: the out-of-vocabulary words and presence of additive noise. We showed that the newly de ned con dence measure performs better in both tests. Review of speech recognition and speaker recognition techniques and some related statistical methods is given through the thesis. We de ned also ...

Recent advances in computer technology and speech and language processing have made possible that some new ways of person-machine communication and computer assistance to human activities start to appear feasible. Concretely, the interest on the development of new challenging applications in indoor environments equipped with multiple multimodal sensors, also known as smart-rooms, has considerably grown. In general, it is well-known that the quality of speech signals captured by microphones that can be located several meters away from the speakers is severely distorted by acoustic noise and room reverberation. In the context of the development of hands-free speech applications in smart-room environments, the use of obtrusive sensors like close-talking microphones is usually not allowed, and consequently, speech technologies must operate on the basis of distant-talking recordings. In such conditions, speech technologies that usually perform reasonably well in free of noise and ...

From artificial voices in GPS to automatic systems of dictation, from voice-based identity verification to voice pathology detection, speech processing applications are nowadays omnipresent in our daily life. By offering solutions to companies seeking for efficiency enhancement with simultaneous cost saving, the market of speech technology is forecast to be especially promising in the next years. The present thesis deals with advances in glottal analysis in order to incorporate new techniques within speech processing applications. While current systems are usually based on information related to the vocal tract configuration, the airflow passing through the vocal folds, and called glottal flow, is expected to exhibit a relevant complementarity. Unfortunately, glottal analysis from speech recordings requires specific complex processing operations, which explains why it has been generally avoided. The main goal of this thesis is to provide new advances in glottal analysis ...

Speech is at the core of human communication. Speaking and listing comes so natural to us that we do not have to think about it at all. The underlying cognitive processes are very rapid and almost completely subconscious. It is hard, if not impossible not to understand speech. For computers on the other hand, recognising speech is a daunting task. It has to deal with a large number of different voices "influenced, among other things, by emotion, moods and fatigue" the acoustic properties of different environments, dialects, a huge vocabulary and an unlimited creativity of speakers to combine words and to break the rules of grammar. Almost all existing automatic speech recognisers use statistics over speech sounds "what is the probability that a piece of audio is an a-sound" and statistics over word combinations to deal with this complexity. The ...

Among all the biomedical signals, speech is among the most complex ones since it is produced and received by humans. The extraction and the analysis of the information conveyed by this signal are the basis of many applications, including the topics discussed in this thesis: the estimation of the glottal source and the automatic detection of voice pathologies. In the first part of the thesis, after a presentation of existing methods for the estimation of the glottal source, a focus is made on the occurence of irregular glottal source estimations when the representation based on the Zeros of the Z-Transform (ZZT) is concerned. As this method is sensitive to the location of the analysis window, it is proposed to regularize the estimation by shifting the analysis window around its initial location. The best shift is found by using a dynamic ...