Recognition

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  • This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumptions that limit their potential effectiveness.

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  • Tăng cường Windows Speech Recognition bằng các Macro Trong bài viết này, chúng ta sẽ thảo luận về Windows Speech Recognition và làm cách nào để tăng cường chức năng của nó bằng cách dùng macro. Chúng ta sẽ học cách làm cách nào tạo macro để làm các việc như: chèn những khối văn bản xác định, chạy những chương trình với tham số xác định và gửi các keystroke đến những ứng dụng bất kỳ.

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  • Details the latest image processing algorithms and imaging systems for image recognition with diverse applications to the military; the transportation, aerospace, information security, and biomedical industries; radar systems; and image tracking systems.This book presents important recent advances in sensors, image processing algorithms, and systems for image recognition and classification with diverse applications in military, aerospace, security, image tracking, radar, biomedical, and intelligent transportation.

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  • 15.7. Ink: Handwriting Recognition In the same way that your grandmother turned yesterday's dinner into today's sandwich (and tomorrow's soup)

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  • 15.4. Speech Recognition Although it may surprise many Mac users, the Mac is quite talented when it comes to speech. Its abilities fall into two categories: reading text aloud

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  • Tham khảo sách 'rolling bearing damage recognition of damage and bearing inspection', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

    pdf76p trinh02 23-01-2013 24 7   Download

  • Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition.

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  • Image recognition and classification is one of the most actively pursued areas in the broad field of imaging sciences and engineering. The reason is evident: the ability to replace human visual capabilities with a machine is very important and there are diverse applications. The main idea is to inspect an image scene by processing data obtained from sensors.

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  • Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field.

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  • This book focuses primarily on speech recognition and the related tasks such as speech enhancement and modeling. This book comprises 3 sections and thirteen chapters written by eminent researchers from USA, Brazil, Australia, Saudi Arabia, Japan, Ireland, Taiwan, Mexico, Slovakia and India. Section 1 on speech recognition consists of seven chapters. Sections 2 and 3 on speech enhancement and speech modeling have three chapters each respectively to supplement section 1.

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  • This paper investigates an aspect of using iris recognition to authenticate a biometric passport. For this kind of authentication, two citizen’s iris will be captured and stored on a RFID (Radio Frequency Identification) chip within two other biometrics: face and fingerprint. This chip is integrated into the cover of a passport, called a biometric passport.

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  • Building an accurate Named Entity Recognition (NER) system for languages with complex morphology is a challenging task. In this paper, we present research that explores the feature space using both gold and bootstrapped noisy features to build an improved highly accurate Arabic NER system.

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  • Large vocabulary speech recognition systems fail to recognize words beyond their vocabulary, many of which are information rich terms, like named entities or foreign words. Hybrid word/sub-word systems solve this problem by adding sub-word units to large vocabulary word based systems; new words can then be represented by combinations of subword units. Previous work heuristically created the sub-word lexicon from phonetic representations of text using simple statistics to select common phone sequences. ...

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  • Arabic handwriting recognition (HR) is a challenging problem due to Arabic’s connected letter forms, consonantal diacritics and rich morphology. In this paper we isolate the task of identification of erroneous words in HR from the task of producing corrections for these words. We consider a variety of linguistic (morphological and syntactic) and non-linguistic features to automatically identify these errors. Our best approach achieves a roughly ∼15% absolute increase in F-score over a simple but reasonable baseline. ...

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  • Turkish is an agglutinative language with complex morphological structures, therefore using only word forms is not enough for many computational tasks. In this paper we analyze the effect of morphology in a Named Entity Recognition system for Turkish. We start with the standard word-level representation and incrementally explore the effect of capturing syntactic and contextual properties of tokens. Furthermore, we also explore a new representation in which roots and morphological features are represented as separate tokens instead of representing only words as tokens. ...

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  • It is important to correct the errors in the results of speech recognition to increase the performance of a speech translation system. This paper proposes a method for correcting errors using the statistical features of character co-occurrence, and evaluates the method. The proposed method comprises two successive correcting processes. The first process uses pairs of strings: the first string is an erroneous substring of the utterance predicted by speech recognition, the second string is the corresponding section of the actual utterance.

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  • We have adapted a classification approach coming from optical character recognition research to the task of speech emotion recognition. The classification approach enjoys the representational power of a syntactic method and efficiency of statistical classification. The syntactic part implements a tree grammar inference algorithm. We have extended this part of the algorithm with various edit costs to penalise more important features with higher edit costs for being outside the interval, which tree automata learned at the inference stage.

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  • It is often claimed that Named Entity recognition systems need extensive gazetteers--lists of names of people, organisations, locations, and other named entities. Indeed, the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named Entity recognition systems. We report on a Named Entity recognition system which combines rule-based grammars with statistical (maximum entropy) models. We report on the system's performance with gazetteers of different types and different sizes, using test material from the MUC-7 competition. ...

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  • Results of computational complexity exist for a wide range of phrase structure-based grammar formalisms, while there is an apparent lack of such results for dependency-based formalisms. We here adapt a result on the complexity of ID/LP-grammars to the dependency framework. Contrary to previous studies on heavily restricted dependency grammars, we prove that recognition (and thus, parsing) of linguistically adequate dependency grammars is~A/T'-complete.

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  • This paper presents a probabilistic framework that combines multiple knowledge sources for Haptic Voice Recognition (HVR), a multimodal input method designed to provide efficient text entry on modern mobile devices. HVR extends the conventional voice input by allowing users to provide complementary partial lexical information via touch input to improve the efficiency and accuracy of voice recognition.

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