Thesis (MPhil (General Linguistics))—University of Stellenbosch, 2009. / Members of churches commonly use the English terms salvation/saved and their isiZulu equivalents insindiso/ukusindiswa. Implied meanings seem to have become attached to these terms, especially in isiZulu, which could cause miscommunication due to the attitudes of superiority of the so-called “saved ones” (abasindisiwe) and consequent antagonism amongst certain ecclesiastical groupings. The question addressed by this study was whether or not the meaning of the term to be saved and its isiZulu translation ukusindiswa, as understood by a selection of isiZulu-speaking Christians, is unambiguous. A further question was whether – should it be the case that these terms are found to be ambiguous – to be saved and its isiZulu translation ukusindiswa could be rehabilitated. Nine people from various denominational backgrounds, both lay and ordained, were interviewed in order to discover how they understood the terms in question. The interviewees were asked ten question, including questions on the influence of cultural practices on the meaning of the terms. These cultural practices were in connection with ancestors, as experienced in Zulu culture, and the influence of their understanding of the terms on the permissibility of ancestral practices. The answers given by the interviewees revealed certain trends. One of them was that, for some isiZulu speakers, the meaning of the terms included the aspect of laying aside of all contact with the ancestors. Those who understood the terms in this manner were seen by the interviewees as having an attitude of superiority and as condemning members of more traditional churches for their adherence to Zulu culture. A sociolinguistic analysis of the terms salvation/insindiso and to be saved/ukusindiswa is presented based on the interviewees’ responses. A conclusion is that the terms are often used in a biased and/or “loaded” way, which is a principal cause of miscommunication and misunderstanding. Ways of reducing this misunderstanding are proposed, including the “rehabilitation” of the terms linguistically and theologically. Greater sensitivity to different ecclesiastical cultures should be shown, involving the use of inclusive language and the exercising of the skills of intercultural communicative competence. This study reveals that the church needs to work at the issues surrounding the terms in question, the use of which can cause a breakdown in intercultural communication.

Atualmente existe uma grande quantidade de dados multimídia sendo geradas todos os dias. Estes dados são oriundos de diversas fontes, como transmissões de rádio ou televisão, gravações de palestras, encontros, conversas telefônicas, vídeos e fotos capturados por celular, entre outros. Com isto, nos últimos anos o interesse pela transcrição de dados multimídia tem crescido, onde, no processamento de voz, podemos destacar as áreas de Reconhecimento de Locutor, Reconhecimento de Fala, Diarização de Locutor e Rastreamento de Locutores. O desenvolvimento destas áreas vem sendo impulsionado e direcionado pelo NIST, que periodicamente realiza avaliações sobre o estado-da-arte. Desde 2000, a tarefa de Diarização de Locutor tem se destacado como uma das principáis frentes de pesquisa em transcrição de dados de voz, tendo sido avaliada pelo NIST por diversas vezes na última década. O objetivo desta tarefa é encontrar o número de locutores presentes em um áudio, e rotular seus respectivos trechos de fala, sem que nenhuma informação tenha sido previamente fornecida. Em outras palavras, costuma-se dizer que o objetivo é responder a questão "Quem falou e quando?". Um dos grandes problemas nesta área é se conseguir obter um bom modelo para cada locutor presente no áudio, dada a pouca quantidade de informações e a alta dimensionalidade dos dados. Neste trabalho, além da criação de um Sistema de Diarização de Locutor, iremos tratar este problema mediante à redução de dimensionalidade através de análises estatísticas. Usaremos a Análise de Componentes Principáis, a Análise de Discriminantes Lineares e a recém apresentada Análise de Semi-Discriminantes Lineares. Esta última utiliza um método de inicialização estático, iremos propor o uso de um método dinâmico, através da detecção de pontos de troca de locutor. Também investigaremos o comportamento destas análises sob o uso simultâneo de múltiplas parametrizações de curto prazo do sinal acústico. Os resultados obtidos mostram que é possível preservar - ou até melhorar - o desempenho do sistema, mesmo reduzindo substâncialmente o número de dimensões. Isto torna mais rápida a execução de algoritmos de Aprendizagem de Máquina e reduz a quantidade de memória necessária para armezenar os dados. / Currently, there is a large amount of multimedia data being generated everyday. These data come from various sources, such as radio or television, recordings of lectures and meetings, telephone conversations, videos and photos captured by mobile phone, among others. Because of this, interest in automatic multimedia data transcription has grown in recent years, where, for voice processing, we can highlight the areas of Speaker Recognition, Speech Recognition, Speaker Diarization and Speaker Tracking. The development of such areas is being conducted by NIST, which periodically promotes state-of-the-art evaluations. Since 2000, the task of Speaker Diarization has emerged as one of the main research fields in voice data transcription, having been evaluated by NIST several times in the last decade. The objective of this task is to find the number of speakers in an audio recording, and properly label their speech segments without the use of any training information. In other words , it is said that the goal of Speaker Diarization is to answer the question "Who spoke when?". A major problem in this area is to obtain a good speaker model from the audio, given the limited amount of information available and the high dimensionality of the data. In the current work, we will describe how our Speaker Diarization System was built, and we will address the problem mentioned by lowering the dimensionality of the data through statistical analysis. We will use the Principal Component Analysis, the Linear Discriminant Analysis and the newly presented Fisher Linear Semi-Discriminant Analysis. The latter uses a static method for initialization, and here we propose the use of a dynamic method by the use of a speaker change points detection algorithm. We also investigate the behavior of these data analysis techniques under the simultaneous use of multiple short term features. Our results show that it is possible to maintain - and even improve - the system performance, by substantially reducing the number of dimensions. As a consequence, the execution of Machine Learning algorithms is accelerated while reducing the amount of memory required to store the data.

Atualmente existe uma grande quantidade de dados multimídia sendo geradas todos os dias. Estes dados são oriundos de diversas fontes, como transmissões de rádio ou televisão, gravações de palestras, encontros, conversas telefônicas, vídeos e fotos capturados por celular, entre outros. Com isto, nos últimos anos o interesse pela transcrição de dados multimídia tem crescido, onde, no processamento de voz, podemos destacar as áreas de Reconhecimento de Locutor, Reconhecimento de Fala, Diarização de Locutor e Rastreamento de Locutores. O desenvolvimento destas áreas vem sendo impulsionado e direcionado pelo NIST, que periodicamente realiza avaliações sobre o estado-da-arte. Desde 2000, a tarefa de Diarização de Locutor tem se destacado como uma das principáis frentes de pesquisa em transcrição de dados de voz, tendo sido avaliada pelo NIST por diversas vezes na última década. O objetivo desta tarefa é encontrar o número de locutores presentes em um áudio, e rotular seus respectivos trechos de fala, sem que nenhuma informação tenha sido previamente fornecida. Em outras palavras, costuma-se dizer que o objetivo é responder a questão "Quem falou e quando?". Um dos grandes problemas nesta área é se conseguir obter um bom modelo para cada locutor presente no áudio, dada a pouca quantidade de informações e a alta dimensionalidade dos dados. Neste trabalho, além da criação de um Sistema de Diarização de Locutor, iremos tratar este problema mediante à redução de dimensionalidade através de análises estatísticas. Usaremos a Análise de Componentes Principáis, a Análise de Discriminantes Lineares e a recém apresentada Análise de Semi-Discriminantes Lineares. Esta última utiliza um método de inicialização estático, iremos propor o uso de um método dinâmico, através da detecção de pontos de troca de locutor. Também investigaremos o comportamento destas análises sob o uso simultâneo de múltiplas parametrizações de curto prazo do sinal acústico. Os resultados obtidos mostram que é possível preservar - ou até melhorar - o desempenho do sistema, mesmo reduzindo substâncialmente o número de dimensões. Isto torna mais rápida a execução de algoritmos de Aprendizagem de Máquina e reduz a quantidade de memória necessária para armezenar os dados. / Currently, there is a large amount of multimedia data being generated everyday. These data come from various sources, such as radio or television, recordings of lectures and meetings, telephone conversations, videos and photos captured by mobile phone, among others. Because of this, interest in automatic multimedia data transcription has grown in recent years, where, for voice processing, we can highlight the areas of Speaker Recognition, Speech Recognition, Speaker Diarization and Speaker Tracking. The development of such areas is being conducted by NIST, which periodically promotes state-of-the-art evaluations. Since 2000, the task of Speaker Diarization has emerged as one of the main research fields in voice data transcription, having been evaluated by NIST several times in the last decade. The objective of this task is to find the number of speakers in an audio recording, and properly label their speech segments without the use of any training information. In other words , it is said that the goal of Speaker Diarization is to answer the question "Who spoke when?". A major problem in this area is to obtain a good speaker model from the audio, given the limited amount of information available and the high dimensionality of the data. In the current work, we will describe how our Speaker Diarization System was built, and we will address the problem mentioned by lowering the dimensionality of the data through statistical analysis. We will use the Principal Component Analysis, the Linear Discriminant Analysis and the newly presented Fisher Linear Semi-Discriminant Analysis. The latter uses a static method for initialization, and here we propose the use of a dynamic method by the use of a speaker change points detection algorithm. We also investigate the behavior of these data analysis techniques under the simultaneous use of multiple short term features. Our results show that it is possible to maintain - and even improve - the system performance, by substantially reducing the number of dimensions. As a consequence, the execution of Machine Learning algorithms is accelerated while reducing the amount of memory required to store the data.

Atualmente existe uma grande quantidade de dados multimídia sendo geradas todos os dias. Estes dados são oriundos de diversas fontes, como transmissões de rádio ou televisão, gravações de palestras, encontros, conversas telefônicas, vídeos e fotos capturados por celular, entre outros. Com isto, nos últimos anos o interesse pela transcrição de dados multimídia tem crescido, onde, no processamento de voz, podemos destacar as áreas de Reconhecimento de Locutor, Reconhecimento de Fala, Diarização de Locutor e Rastreamento de Locutores. O desenvolvimento destas áreas vem sendo impulsionado e direcionado pelo NIST, que periodicamente realiza avaliações sobre o estado-da-arte. Desde 2000, a tarefa de Diarização de Locutor tem se destacado como uma das principáis frentes de pesquisa em transcrição de dados de voz, tendo sido avaliada pelo NIST por diversas vezes na última década. O objetivo desta tarefa é encontrar o número de locutores presentes em um áudio, e rotular seus respectivos trechos de fala, sem que nenhuma informação tenha sido previamente fornecida. Em outras palavras, costuma-se dizer que o objetivo é responder a questão "Quem falou e quando?". Um dos grandes problemas nesta área é se conseguir obter um bom modelo para cada locutor presente no áudio, dada a pouca quantidade de informações e a alta dimensionalidade dos dados. Neste trabalho, além da criação de um Sistema de Diarização de Locutor, iremos tratar este problema mediante à redução de dimensionalidade através de análises estatísticas. Usaremos a Análise de Componentes Principáis, a Análise de Discriminantes Lineares e a recém apresentada Análise de Semi-Discriminantes Lineares. Esta última utiliza um método de inicialização estático, iremos propor o uso de um método dinâmico, através da detecção de pontos de troca de locutor. Também investigaremos o comportamento destas análises sob o uso simultâneo de múltiplas parametrizações de curto prazo do sinal acústico. Os resultados obtidos mostram que é possível preservar - ou até melhorar - o desempenho do sistema, mesmo reduzindo substâncialmente o número de dimensões. Isto torna mais rápida a execução de algoritmos de Aprendizagem de Máquina e reduz a quantidade de memória necessária para armezenar os dados. / Currently, there is a large amount of multimedia data being generated everyday. These data come from various sources, such as radio or television, recordings of lectures and meetings, telephone conversations, videos and photos captured by mobile phone, among others. Because of this, interest in automatic multimedia data transcription has grown in recent years, where, for voice processing, we can highlight the areas of Speaker Recognition, Speech Recognition, Speaker Diarization and Speaker Tracking. The development of such areas is being conducted by NIST, which periodically promotes state-of-the-art evaluations. Since 2000, the task of Speaker Diarization has emerged as one of the main research fields in voice data transcription, having been evaluated by NIST several times in the last decade. The objective of this task is to find the number of speakers in an audio recording, and properly label their speech segments without the use of any training information. In other words , it is said that the goal of Speaker Diarization is to answer the question "Who spoke when?". A major problem in this area is to obtain a good speaker model from the audio, given the limited amount of information available and the high dimensionality of the data. In the current work, we will describe how our Speaker Diarization System was built, and we will address the problem mentioned by lowering the dimensionality of the data through statistical analysis. We will use the Principal Component Analysis, the Linear Discriminant Analysis and the newly presented Fisher Linear Semi-Discriminant Analysis. The latter uses a static method for initialization, and here we propose the use of a dynamic method by the use of a speaker change points detection algorithm. We also investigate the behavior of these data analysis techniques under the simultaneous use of multiple short term features. Our results show that it is possible to maintain - and even improve - the system performance, by substantially reducing the number of dimensions. As a consequence, the execution of Machine Learning algorithms is accelerated while reducing the amount of memory required to store the data.

Atualmente, diversos trabalhos e aplicações são desenvolvidos com foco na área de reconhecimento computacional de locutores. À medida que o interesse por diversas aplicações reais dentro dessa área emerge, principalmente em biometria, na qual a segurança e a eficácia são de extrema importância, torna-se cada vez mais necessário que estudos sejam feitos, na mesma proporção, visando avaliá-las. Desse modo, a proposta do presente trabalho é a de mensurar a acurácia de um sistema de reconhecimento de locutores baseado em características elementares, isto é, energias de sub-bandas de frequências, em associação com um classificador probabilístico, estudando a viabilidade de extraí-las das transições entre trechos vozeados e não-vozeados (TTVNV) dos sinais. Testes são realizados com diferentes quantidades de locutores e discurso fixado. A acurácia obtida nos testes variam de 20.18% a 92.53%. Os resultados obtidos são comparados e relatados, complementando as afirmações existentes na literatura sobre o uso das TTVNV com dados quantitativos. / Nowadays, many works and applications are developed focusing on computational speaker recognition. As the interest for several real applications within this area emerges, especially in biometrics, where the safety and the efficacy of the applications are extremely important, studies need to be developed in the same proportion, to evaluate the effectiveness of such approaches. Based on that, this work intends to measure the accuracy of a speaker recognition system that uses elementar features, i.e., sub-band frequency energies, associated with a probabilistic classifier, studying the viability of extracting them from the transition between voiced and unvoiced speech tags (TTVNV). Tests are carried out with different numbers of speakers and a text-dependent approach. The accuracy of the tests varies from 20.18% to 92.53%. The results are compared and reported, complementing the existent information on the use of TTVNV with quantitative data.

Cette recherche opère un renversement du mode de construction de l’objet de la recherche en sciences du langage. Elle tente de comprendre, à travers une enquête sociolinguistique par entretiens, le rapport subjectif de jeunes locuteurs algériens à l’égard de leurs langues (algérienne, arabe, tamazight, français). Bien que les attitudes à l’égard de ces langues se sont imposées à l’analyse pour préciser le statut particulièrement complexe des langues en usage en Algérie, cette recherche tente plus particulièrement de comprendre un rapport souvent considéré comme allant de soi : le rapport subjectif à leur langue dite de « religion », « la langue arabe » (sans autre précision). Nombreuses sont les questions qui ont animées l’enquête sociolinguistique et anthropologique à partir de laquelle part le questionnement centré sur le mythe du sacré dans la langue arabe.Intrinsèquement liée au Coran, ce corps d’une ‘Umma imaginaire à laquelle tout algérien s’imagine appartenir ou ne pas appartenir, la langue arabe suscite des positionnements ambivalents chez ses tenants : quelles sont les positions subjectives d’étudiants algériens motivés par la réussite, mus par leurs rêves, à l’égard de la langue arabe de religion ? La dimension diglossique qui inspire les désignations fluctuantes « langue/dialecte » entraineraient-elles des spécificités chez les locuteurs arabophones et/ou berbérophones algériens ? Si oui, quelles attitudes et représentations renferme cette idéologie dieu-glossique ? Les locuteurs assument-ils la désignation « sacré » associée à la langue arabe ? Cet imaginaire linguistique (« langue d’Adam, du Paradis, pure, NOtre langue, langue de l’intercompréhension ») serait-il de nature à influencer les comportements linguistiques des locuteurs arabophones ? En effet, qui mieux qu’un locuteur arabophone pour expliquer le clivage entre une sorte de respect exagéré de la forme de ce qui est désignée « langue » d’un côté, en même temps, sa stigmatisation de l’autre ? Serait-ce de l’ordre du fétichisme de la langue ? Le traitement de ces questions révèle la manière dont chacun et chacune des jeunes locuteurs et locutrices algériennes interrogé.e.s exprime son rapport subjectif à sa langue de religion et de scolarisation : sublime pour l’un, horrible pour l’Autre, la langue sacrée a « plus d’un tour dans son sacre ». / This research operates a change in the mode of construction of research object in the sciences of Linguistics. It tries to understand, through an anthropological and sociolinguistic survey by interviews, the subjective relationship of young Algerian speakers with regard to their languages (Algerian, Arabic, Tamazight, French). Although attitudes towards these languages have been imposed on the analysis in order to clarify the particularly complex status of the languages used in Algeria, this research attempts more particularly to understand a relationship often taken for granted: the subjective relationship to their so-called language of "religion", "the Arabic language" (without further specification). Many questions have prompted the sociolinguistic and anthropological inquiry from which the questioning centered on the myth of the sacred in the Arabic language begins.Intrinsically linked to the Koran, this body of an imaginary Umma to which all Algerian Arabic and Berber speaker imagine belonging to or not, the Arabic language creates ambivalent positions among its speakers. What are the subjectives positions of Algerian students motivated by success, driven by their dreams, with regard to the Arabic language of religion? Does the multi-diglossic dimension that inspires the fluctuating positions ("language / dialect") designations lead to specificities among Algerian-speaking and / or Berber-speaking speakers? If so, what attitudes and representations contain this god-glossic ideology? Do the speakers assume the "sacred" designation associated with the Arabic language? Is the linguistic imagination (the language of Adam, of Paradise, pure, our language, the language of intercomprehension) likely to influence the linguistic behavior of Arabic speakers? Indeed, who better than an Arabic speaker to explain the cleavage between a kind of exaggerated respect for the form of what is designated "language" on one side, at the same time, its stigmatization on the other. Could it be the order of the fetishism of language? Answering these questions reveals how each of the young Algerian speakers interviewed expresses their subjective relationship to their language of religion and schooling: sublime for one, horrible for the Other, the sacred language has "More than one trick up its sleeve "

Le domaine de la reconnaissance automatique du locuteur (RAL) a vu des avancées considérables dans la dernière décennie permettant d’atteindre des taux d’erreurs très faibles dans des conditions contrôlées. Cependant, l’implémentation de cette technologie dans des applications réelles est entravée par la grande dégradation des performances en présence de nuisances acoustiques en phase d’utilisation. Un grand effort a été investi par la communauté de recherche en RAL dans la conception de techniques de compensation des nuisances acoustiques. Ces techniques opèrent à différents niveaux : signal, paramètres acoustiques, modèles ou scores. Avec le développement du paradigme de "variabilité totale", de nouvelles possibilités peuvent être explorées profitant des propriété statistiques simples de l’espace des i-vecteurs. Notre travail de thèse s’inscrit dans ce cadre et propose des techniques de compensation des nuisances acoustiques qui opèrent directement dans le domaine des i-vecteurs. Ces algorithmes utilisent des relations simples entre les i-vecteurs corrompus et leurs versions propres et font abstraction de l’effet réel des nuisances dans cet espace. Afin de mettre en œuvre cette méthodologie, des exemples de données propres / corrompues sont générés artificiellement et utilisés pour construire des algorithmes de compensation des nuisances acoustiques. Ce procédé permet d’éviter les dérivations qui peuvent être complexes, voire très approximatives. Les techniques développées dans cette thèse se divisent en deux classes : La première classe de techniques se base sur un modèle de distorsion dans le domaine des i-vecteurs. Une relation entre la version propre et la version corrompue d’un i-vecteur est posée et un estimateur permettant de transformer un i-vecteur de test corrompu en sa version propre est construit. La deuxième classe de techniques n’utilise aucun modèle de distorsion dans le domaine des i-vecteurs. Elle permet de tenir compte à la fois de la distribution des i-vecteurs propres, corrompus ainsi que la distribution jointe. Des expériences ont été réalisées sur les données bruitées ainsi que les données de courte durée ; donnés de NIST SRE 2008 bruitées/découpées artificiellement ainsi que les données du challenge SITW bruitées naturellement / de courte durée. / Speaker recognition witnessed considerable progress in the last decade, achieving very low error rates in controlled conditions. However, the implementation of this technology in real applications is hampered by the great degradation of performances in presence of acoustic nuisances. A lot of effort has been invested by the research community in the design of nuisance compensation techniques in the past years. These algorithms operate at different levels : signal, acoustic parameters, models or scores. With the development of the "total variability" paradigm, new possibilities can be explored due to the simple statistical properties of the i-vector space. Our work falls within this framework and presents new compensation techniques which operate directly in the i-vector space. These algorithms use simple relationships between corrupted i-vectors and the corresponding clean versions and ignore the real effect of nuisances in this domain. In order to implement this methodology, pairs of clean and corrupted data are artificially generated then used to develop nuisance compensation algorithms. This method avoids making complex derivations and approximations. The techniques developed in this thesis are divided into two classes : The first class of techniques is based on a distortion model in the i-vector space. A relationships between the clean version of an i-vector and its corrupted version is set and an estimator is built to transform a corrupted test i-vector to its clean counterpart. The second class of techniques does not use any distortion model in the i-vectors domain. It takes into account both the distribution of the clean, corrupt i-vectors as well as the joint distribution. Experiments are carried-out on noisy data and short utterances ; artificially corrupted NIST SRE 2008 data and natural SITW (short / noisy segments).

Even though speaker verification is a broad subject, the commercial and personal use implementations are rare. There are several problems that need to be solved before speaker verification can become more useful. The amount of pattern matching and feature extraction techniques is large and the decision on which ones to use is debatable. One of the main problems of speaker verification in general is the impact of noise. The very popular feature extraction technique MFCC is inherently sensitive to mismatch between training and verification conditions. MFCC is used in many speech recognition applications and is not only useful in text-dependent speaker verification. However the most reliable verification techniques are text-dependent. One of the most popular pattern matching techniques in text-dependent speaker verification is DTW. Although having limitations outside the text-dependent applications it is a reliable way of matching templates even with limited amount of training material. The signal processing techniques, MFCC and DTW are explained and discussed in detail along with a Matlab program where these techniques have been implemented. The choices made in signal processing, feature extraction and pattern matching  are determined by discussions of available studies on these topics. The results indicate that it is possible to program text-dependent speaker verification systems that are functional in clean conditions with tools like Matlab.

La notion d’insécurité linguistique a été régulièrement explorée depuis les années 1960: les recherches ont été initialement centrées autour des questions de prononciation dans différents milieux sociaux ; ensuite un cadre d’analyse a été organisé autour des communautés francophones dites « périphériques » ; elle a enfin été abordée dans des contextes plurilingues. La présente recherche examine la notion d’insécurité linguistique comme elle est vécue par les professeurs non natifs de langues étrangères, et notamment des professeurs grecs de français. Ce groupe socioprofessionnel joue un rôle de premier plan dans la diffusion des langues : l’enjeu est de trouver des moyens d’atténuer les éventuels effets négatifs de l’insécurité linguistique inhérents à leur contexte professionnel. Pour ce faire, nous avons construit un corpus pour une étude qualitative. La méthodologie retenue a été celle d’entretiens individuels, semi dirigés, permettant à des répondants présentant des profils divers en termes d’âge, de sexe, de formation, de lieux et de contextes professionnels, de s’exprimer sur leur conception de l’insécurité linguistique et sur les stratégies mobilisées pour y faire face. De manière assez constante, émerge la question de la légitimité d’enseigner une langue dont on n’est pas locuteur natif et les difficultés que cela pose dans le contexte professionnel. Pourtant, au terme d’un processus plus ou moins long, ces personnes parviennent, en redéfinissant leur rôle dans la classe et parfois leurs objectifs en tant qu’enseignants, à gagner en assurance. Elles reconstruisent une légitimité qui articule acceptation, remédiation des imperfections et compétence professionnelle. / The concept of linguistic insecurity has been regularly explored since the 1960’s : research was initially centered around the questions of pronunciation in various social environments; then a framework of analysis was organized around French-speaking communities known as “peripheral”; it was finally approached in multilingual contexts. This research examines the notion of linguistic insecurity as it is experienced by non-native foreign languages teachers, and in particular Greek teachers of French. This socio-professional group plays a leading role in the diffusion of the languages : the stake is to find the means to moderate the possible negative effects of linguistic insecurity inherent in their professional context. With this intention, a corpus for a qualitative study has been assembled. The methodology selected was that of individual, semi-structured interviews, allowing interviewees of various profiles in terms of age, sex, training, professional places and contexts to express themselves on their conception of the linguistic insecurity and the strategies mobilized to face it. In a rather constant way emerges the question of legitimacy to teach a language of which one is not a native speaker and the difficulties that this poses in the professional context. However, at the end of a more or less long process, these people arrive, by redefining their role in the class and sometimes their objectives as teachers, to gain in confidence. They rebuild a legitimacy which articulates acceptance, remediation of the imperfections and professional competence.

The present Master's thesis deals with the forensic use of fundamental frequency characteristics, specifically with F0 mean values and indicators of variability. Phoneticians who specialise in the forensic analysis of speech generally believe that F0 does not hold much potential as a parameter useful for speaker identification, mainly because it is easily influenced by extrinsic factors (e.g. the speaker's emotional state, interfering noise, transmission channel or even the speaker's own effort to mask his voice), which cause high intra-individual variability. Despite these facts, however, the forensic use of F0 offers a number of advantages, namely straightforward extraction from the speech signal and lower susceptibility to varying lexical content - unlike, for example, vowel formants. This thesis investigates the recordings of 8 male speakers made in two different speech styles (spontaneous and read) and compares the respective indicators of F0 stability and variability, in particular those that are robust in varying external conditions: that is, the baseline for mean values and the 10.-90. percentile range for variability indicators. Apart from that, we take into account phenomena such as the creaky voice, which are idiosyncratic and contribute to easier speaker discrimination. Key words:...