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Language Identification (LID) is the task of automatically identifying the language of speech signal uttered by an unknown speaker. An N language LID task is to classify an input speech utterance, spoken by an unknown speaker and of unknown text, as belonging to one of the N languages L1, L2, . . , LN. We present a new approach to spoken language modeling for language identification using the Lempel-Ziv-Welch (LZW) algorithm, with which we try to overcome the limitations of n-gram stochastic models by automatically identifying the valid set of variable length patterns from the training data. However, since several patterns in a language pattern table are also shared by other language pattern tables, confusability prevailed in the LID task. To overcome this, three pruning techniques are proposed to make these pattern tables more language specific. For LID with limited training data, we present another language modeling technique, which compensates for language specific patterns missing in the language specific LZW pattern table. We develop two new discriminative measures for LID based on the LZW algorithm, viz., (i) Compression Ratio Score (LZW-CRS) and (ii) Weighted Discriminant Score (LZW-WDS). It is shown that for a 6-language LID task of the OGI-TS database, the new model (LZW-WDS) significantly outperforms the conventional bigram approach. With regard to the front end of the LID system, we develop a modified technique to model for Acoustic Sub-Word Units (ASWU) and explore its effectiveness. The segmentation of speech signal is done using an acoustic criterion (ML-segmentation). However, we believe that consistency and discriminability among speech units is the key issue for the success of ASWU based speech processing. We develop a new procedure for clustering and modeling the segments using sub-word GMMs. Because of the flexibility in choosing the labels for the sub-word units, we do an iterative re-clustering and modeling of the segments. Using a consistency measure of labeling the acoustic segments, the convergence of iterations is demonstrated. We show that the performance of new ASWU based front-end and the new LZW based back-end for LID outperforms the earlier reported PSWR based LID.

It has been increasingly recognized that realistic problems often involve a tradeoff among many conflicting objectives. Traditional methods aim at satisfying multiple objectives by combining them into a global cost function, which in most cases overlooks the underlying tradeoffs between the conflicting objectives. This raises the issue about how different objectives should be combined to yield a final solution. Moreover, such approaches promise that the chosen overall objective function is optimized over the training samples. However, there is no guarantee on the performance in terms of the individual objectives since they are not considered on an individual basis. Motivated by these shortcomings of traditional methods, the objective in this dissertation is to investigate theory, algorithms, and applications for problems with competing objectives and to understand the behavior of the proposed algorithms in light of some applications. We develop a multi-objective programming (MOP) framework for finding compromise solutions that are satisfactory for each of multiple competing performance criteria. The fundamental idea for our formulation, which we refer to as iterative constrained optimization (ICO), evolves around improving one objective while allowing the rest to degrade. This is achieved by the optimization of individual objectives with proper constraints on the remaining competing objectives. The constraint bounds are adjusted based on the objective functions obtained in the most recent iteration. An aggregated utility function is used to evaluate the acceptability of local changes in competing criteria, i.e., changes from one iteration to the next. Conflicting objectives arise in different contexts in many problems of speech and language technologies. In this dissertation, we consider two applications. The first application is language model (LM) adaptation, where a general LM is adapted to a specific application domain so that the adapted LM is as close as possible to both the general model and the application domain data. Language modeling and adaptation is used in many speech and language processing applications such as speech recognition, machine translation, part-of-speech tagging, parsing, and information retrieval. The second application is automatic language identification (LID), where the standard detection performance evaluation measures false-rejection (or miss) and false-acceptance (or false alarm) rates for a number of languages are to be simultaneously minimized. LID systems might be used as a pre-processing stage for understanding systems and for human listeners, and find applications in, for example, a hotel lobby or an international airport where one might speak to a multi-lingual voice-controlled travel information retrieval system. This dissertation is expected to provide new insights and techniques for accomplishing significant performance improvement over existing approaches in terms of the individual competing objectives. Meantime, the designer has a better control over what is achieved in terms of the individual objectives. Although many MOP approaches developed so far are formal and extensible to large number of competing objectives, their capabilities are examined only with two or three objectives. This is mainly because practical problems become significantly harder to manage when the number of objectives gets larger. We, however, illustrate the proposed framework with a larger number of objectives.

Made available in DSpace on 2014-12-17T15:04:59Z (GMT). No. of bitstreams: 1 PabloJSL_DISSERT.pdf: 2748711 bytes, checksum: 25bd52a895ba4efa08fa7fde9c4d4718 (MD5) Previous issue date: 2012-08-10 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This paper discusses aspects related to the mathematical language and its understanding, in particular, by students of final years of elementary school. Accordingly, we aimed to develop a proposal for teaching, substantiated by mathematical modeling activities and reading, which takes advantage of the student of elementary school a better understanding of mathematical language for the content of proportion. We also aim to build / propose parameters for the assessment of reading proficiency of the language of the student in analyzing and modeling process, its ability to develop/improve/enhance this proficiency. For this purpose, we develop a qualitative research, with procedures for an action research whose analysis of the data is configured as Content Analysis. We refer to epistemological and didactic, in the studies: Piaget (1975, 1990), Vygotsky (1991, 2001), Bakhtin (2006), Freire (1974, 1994), Bicudo and Garnica (2006), Smole and Diniz (2001), Barbosa (2001), Burak (1992), Biembengut (2004), Bassanezi (2002), Carrasco (2006), Becker (2010), Zuin and Reyes (2010), among others. We understand that to acquire new knowledge one must learn to read and reading to learn it, this process is essential for the development of reading proficiency of a person. Modeling, in turn, is a process which enables contact with different forms of reading providing elements favorable to the development here mentioned. The evaluation parameters we use to analyze the level of reading proficiency of mathematical language proved to be effective and therefore a valuable tool that allows the teacher an efficient evaluation and whose results can guide you better in the planning and execution of their practice / Este trabalho discute aspectos relacionados ? linguagem matem?tica e ? sua compreens?o, em especial, por estudantes dos anos finais do Ensino Fundamental. Nesse sentido, objetivamos elaborar uma proposta de ensino consubstanciada por atividades de modelagem matem?tica e de leitura, que oportunize ao aluno do Ensino Fundamental uma melhor compreens?o da linguagem matem?tica inerente ao conte?do de propor??o. Visamos tamb?m construir/propor par?metros para a avalia??o da profici?ncia leitora desta linguagem por parte do estudante e analisar no processo de modelagem, a sua capacidade de desenvolver/aprimorar/potencializar esta profici?ncia. Para isso, desenvolvemos uma pesquisa de cunho qualitativo, com procedimentos de uma pesquisa-a??o, cuja an?lise dos dados se configura como An?lise de Conte?do. Referenciamo-nos epistemologicamente e didaticamente, nos estudos de: Piaget (1975, 1990), Vygotsky (1991, 2001), Bakhtin (2006), Freire (1974, 1994), Bicudo e Garnica (2006), Smole e Diniz (2001), Barbosa (2001), Burak (1992), Biembengut (2004), Bassanezi (2002), Carrasco (2006), Becker (2010), Zuin e Reyes (2010), dentre outros. Entendemos que para adquirir um novo conhecimento ? preciso aprender a l?-lo e ler para aprend?-lo, este processo ? indispens?vel para o desenvolvimento da profici?ncia leitora de um sujeito. A modelagem, por sua vez, ? um processo que possibilita o contato com distintas formas de leitura, oferecendo elementos favor?veis ao desenvolvimento ora mencionado. Os par?metros avaliativos que utilizamos para analisar o n?vel de profici?ncia leitora da linguagem matem?tica mostrou-se eficaz e, portanto, um valioso instrumento que permite ao professor uma avalia??o eficiente e cujos resultados podem orient?-lo melhor no planejamento e execu??o de sua pr?tica

Grammatical Error Correction refers to the task of automatically correcting errors in written text, typically with respect to texts written by learners of a second language. The work in this thesis implements and evaluates two methods to Grammatical Error Correction for Swedish. In addition, the proposed methods are compared to an existing, rule-based system. Previous research on GEC for the Swedish language is limited and has not yet utilized the potential of neural networks. The first method implemented in this work is based on a neural machine translation approach, training a Transformer model to translate erroneous text into a corrected version. A parallel dataset containing artificially generated errors is created to train the model. The second method utilizes a Swedish version of the pre-trained language model BERT to estimate the likelihood of potential corrections in an erroneous text. Employing the SweLL gold corpus consisting of essays written by learners of Swedish, the proposed methods are evaluated using GLEU and through a manual evaluation based on the types of errors and their corresponding corrections found in the essays. The results show that the two methods correct approximately the same amount of errors, while differing in terms of which error types that are best handled. Specifically, the translation approach has a wider coverage of error types and is superior for syntactical and punctuation errors. In contrast, the language model approach yields consistently higher recall and outperforms the translation approach with regards to lexical and morphological errors. To improve the results, future work could investigate the effect of increased model size and amount of training data, as well as the potential in combining the two methods.

This thesis presents training of an end-to-end autoencoder model using the transformer, with an encoder that can encode sentences into fixed-length latent vectors and a decoder that can reconstruct the sentences using image representations. Encoding and decoding sentences to and from these image representations are central to the model design. This method allows new sentences to be generated by traversing the Euclidean space, which makes vector arithmetic possible using sentences. Machines excel in dealing with concrete numbers and calculations, but do not possess an innate infrastructure designed to help them understand abstract concepts like natural language. In order for a machine to process language, scaffolding must be provided wherein the abstract concept becomes concrete. The main objective of this research is to provide such scaffolding so that machines can process human language in an intuitive manner.

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Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal