875 research outputs found

    Prosody in text-to-speech synthesis using fuzzy logic

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    For over a thousand years, inventors, scientists and researchers have tried to reproduce human speech. Today, the quality of synthesized speech is not equivalent to the quality of real speech. Most research on speech synthesis focuses on improving the quality of the speech produced by Text-to-Speech (TTS) systems. The best TTS systems use unit selection-based concatenation to synthesize speech. However, this method is very timely and the speech database is very large. Diphone concatenated synthesized speech requires less memory, but sounds robotic. This thesis explores the use of fuzzy logic to make diphone concatenated speech sound more natural. A TTS is built using both neural networks and fuzzy logic. Text is converted into phonemes using neural networks. Fuzzy logic is used to control the fundamental frequency for three types of sentences. In conclusion, the fuzzy system produces f0 contours that make the diphone concatenated speech sound more natural

    Symbol Emergence in Robotics: A Survey

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    Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

    Neurocognitive Informatics Manifesto.

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    Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

    Speaker Recognition Using Multiple Parametric Self-Organizing Maps

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    Speaker Recognition is the process of automatically recognizing a person who is speaking on the basis of individual parameters included in his/her voice. This technology allows systems to automatically verify identify in applications such as banking by telephone or forensic science. A Speaker Recognition system has the following main modules: Feature Extraction and Classification. For feature extraction the most commonly used techniques are MEL-Frequency Cepstrum Coefficients (MFCC) and Linear Predictive Coding (LPC). For classification and verification, technologies such as Vector Quantization (VQ), Hidden Markov Models (HMM) and Neural Networks have been used. The contribution of this thesis is a new methodology to achieve high accuracy identification and impostor rejection. The new proposed method, Multiple Parametric Self-Organizing Maps (M-PSOM) is a classification and verification technique. The new method was successfully implemented and tested using the CSLU Speaker Recognition Corpora of the Oregon School of Engineering with excellent results

    An autopoietic approach to the development of speech recognition (pendekatan autopoietic dalam pembangunan pengecaman suara)

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    The focus of research here is on the implementation of speech recognition through an autopoietic approach. The work done here has culminated in the introduction of a neural network architecture named Homunculus Network. This network was used in the development of a speech recognition system for Bahasa Melayu. The speech recognition system is an isolated-word, phoneme-level speech recognizer that is speaker independent and has a vocabulary of 15 words. The research done has identified some issues worth further work later. These issues are also the basis for the design and the development of the new autopoietic speech recognition system

    Comparison between rule-based and data-driven natural language processing algorithms for Brazilian Portuguese speech synthesis

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    Due to the exponential growth in the use of computers, personal digital assistants and smartphones, the development of Text-to-Speech (TTS) systems have become highly demanded during the last years. An important part of these systems is the Text Analysis block, that converts the input text into linguistic specifications that are going to be used to generate the final speech waveform. The Natural Language Processing algorithms presented in this block are crucial to the quality of the speech generated by synthesizers. These algorithms are responsible for important tasks such as Grapheme-to-Phoneme Conversion, Syllabification and Stress Determination. For Brazilian Portuguese (BP), solutions for the algorithms presented in the Text Analysis block have been focused in rule-based approaches. These algorithms perform well for BP but have many disadvantages. On the other hand, there is still no research to evaluate and analyze the performance of data-driven approaches that reach state-of-the-art results for complex languages, such as English. So, in this work, we compare different data-driven approaches and rule-based approaches for NLP algorithms presented in a TTS system. Moreover, we propose, as a novel application, the use of Sequence-to-Sequence models as solution for the Syllabification and Stress Determination problems. As a brief summary of the results obtained, we show that data-driven algorithms can achieve state-of-the-art performance for the NLP algorithms presented in the Text Analysis block of a BP TTS system.Nos últimos anos, devido ao grande crescimento no uso de computadores, assistentes pessoais e smartphones, o desenvolvimento de sistemas capazes de converter texto em fala tem sido bastante demandado. O bloco de análise de texto, onde o texto de entrada é convertido em especificações linguísticas usadas para gerar a onda sonora final é uma parte importante destes sistemas. O desempenho dos algoritmos de Processamento de Linguagem Natural (NLP) presentes neste bloco é crucial para a qualidade dos sintetizadores de voz. Conversão Grafema-Fonema, separação silábica e determinação da sílaba tônica são algumas das tarefas executadas por estes algoritmos. Para o Português Brasileiro (BP), os algoritmos baseados em regras têm sido o foco na solução destes problemas. Estes algoritmos atingem bom desempenho para o BP, contudo apresentam diversas desvantagens. Por outro lado, ainda não há pesquisa no intuito de avaliar o desempenho de algoritmos data-driven, largamente utilizados para línguas complexas, como o inglês. Desta forma, expõe-se neste trabalho uma comparação entre diferentes técnicas data-driven e baseadas em regras para algoritmos de NLP utilizados em um sintetizador de voz. Além disso, propõe o uso de Sequence-to-Sequence models para a separação silábica e a determinação da tonicidade. Em suma, o presente trabalho demonstra que o uso de algoritmos data-driven atinge o estado-da-arte na performance dos algoritmos de Processamento de Linguagem Natural de um sintetizador de voz para o Português Brasileiro

    Induction of the morphology of natural language : unsupervised morpheme segmentation with application to automatic speech recognition

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    In order to develop computer applications that successfully process natural language data (text and speech), one needs good models of the vocabulary and grammar of as many languages as possible. According to standard linguistic theory, words consist of morphemes, which are the smallest individually meaningful elements in a language. Since an immense number of word forms can be constructed by combining a limited set of morphemes, the capability of understanding and producing new word forms depends on knowing which morphemes are involved (e.g., "water, water+s, water+y, water+less, water+less+ness, sea+water"). Morpheme boundaries are not normally marked in text unless they coincide with word boundaries. The main objective of this thesis is to devise a method that discovers the likely locations of the morpheme boundaries in words of any language. The method proposed, called Morfessor, learns a simple model of concatenative morphology (word forming) in an unsupervised manner from plain text. Morfessor is formulated as a Bayesian, probabilistic model. That is, it does not rely on predefined grammatical rules of the language, but makes use of statistical properties of the input text. Morfessor situates itself between two types of existing unsupervised methods: morphology learning vs. word segmentation algorithms. In contrast to existing morphology learning algorithms, Morfessor can handle words consisting of a varying and possibly high number of morphemes. This is a requirement for coping with highly-inflecting and compounding languages, such as Finnish. In contrast to existing word segmentation methods, Morfessor learns a simple grammar that takes into account sequential dependencies, which improves the quality of the proposed segmentations. Morfessor is evaluated in two complementary ways in this work: directly by comparing to linguistic reference morpheme segmentations of Finnish and English words and indirectly as a component of a large (or virtually unlimited) vocabulary Finnish speech recognition system. In both cases, Morfessor is shown to outperform state-of-the-art solutions. The linguistic reference segmentations were produced as part of the current work, based on existing linguistic resources. This has resulted in a morphological gold standard, called Hutmegs, containing analyses of a large number of Finnish and English word forms.reviewe
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