Automatic recognition

Xem 1-20 trên 87 kết quả Automatic recognition
  • In this paper, we present initial experiments in the recognition of deceptive language. We introduce three data sets of true and lying texts collected for this purpose, and we show that automatic classification is a viable technique to distinguish between truth and falsehood as expressed in language. We also introduce a method for class-based feature analysis, which sheds some light on the features that are characteristic for deceptive text.

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  • This paper is a progress report on a project in linguistically based automatic speech recognition, The domain of this project is English intonation. The system I will describe analyzes fundamental frequency contours (F0 contours) of speech in terms of the theory of melody laid out in Pierrehumbert (1980). Experiments discussed in Liberman and Pierrehumbert (1983) support the assumptions made about intonational phonetics, and an F0 synthesis program based on a precursor to the present theory is described in Pierrehumbert (1981). ...

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Experiments on Automatic Recognition of Nonnative Arabic Speech

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  • Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: The automatic recognition and counting of cough...

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  • This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject.

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  • 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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  • This paper describes an approach to extract the aspectual information of Japanese verb phrases from a monolingual corpus. We classify Verbs into six categories by means of the aspectual features which are defined on the basis of the possibility of co-occurrence with aspectual forms and adverbs. A unique category could be identified for 96% of the target verbs. To evaluate the result of the experiment, we examined the meaning of -leiru which is one of the most fundamental aspectual markers in Japanese, and obtained the correct recognition score of 71% for the 200 sentences. ...

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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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  • We demonstrate that transformation-based learning can be used to correct noisy speech recognition transcripts in the lecture domain with an average word error rate reduction of 12.9%. Our method is distinguished from earlier related work by its robustness to small amounts of training data, and its resulting efficiency, in spite of its use of true word error rate computations as a rule scoring function.

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  • Identification of transliterated names is a particularly difficult task of Named Entity Recognition (NER), especially in the Chinese context. Of all possible variations of transliterated named entities, the difference between PRC and Taiwan is the most prevalent and most challenging. In this paper, we introduce a novel approach to the automatic extraction of diverging transliterations of foreign named entities by bootstrapping cooccurrence statistics from tagged and segmented Chinese corpus. Preliminary experiment yields promising results and shows its potential in NLP applications. ...

    pdf4p hongvang_1 16-04-2013 21 2   Download

  • Noun extraction is very important for many NLP applications such as information retrieval, automatic text classification, and information extraction. Most of the previous Korean noun extraction systems use a morphological analyzer or a Partof-Speech (POS) tagger. Therefore, they require much of the linguistic knowledge such as morpheme dictionaries and rules (e.g. morphosyntactic rules and morphological rules).

    pdf8p bunbo_1 17-04-2013 14 2   Download

  • Language modeling is to associate a sequence of words with a priori probability, which is a key part of many natural language applications such as speech recognition and statistical machine translation. In this paper, we present a language modeling based on a kind of simple dependency grammar. The grammar consists of head-dependent relations between words and can be learned automatically from a raw corpus using the reestimation algorithm which is also introduced in this paper. Our experiments show that the proposed model performs better than n-gram models at 11% to 11.

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  • This paper describes recent progress and the author's perspectives of speech recognition technology. Applications of speech recognition technology can be classified into two main areas, dictation and human-computer dialogue systems. In the dictation domain, the automatic broadcast news transcription is now actively investigated, especially under the DARPA project.

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  • In this paper we propose a method to automatically label multi-lingual data with named entity tags. We build on prior work utilizing Wikipedia metadata and show how to effectively combine the weak annotations stemming from Wikipedia metadata with information obtained through English-foreign language parallel Wikipedia sentences.

    pdf9p nghetay_1 07-04-2013 16 1   Download

  • Syntactic knowledge is important for discourse relation recognition. Yet only heuristically selected flat paths and 2-level production rules have been used to incorporate such information so far. In this paper we propose using tree kernel based approach to automatically mine the syntactic information from the parse trees for discourse analysis, applying kernel function to the tree structures directly.

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  • Written documents created through dictation differ significantly from a true verbatim transcript of the recorded speech. This poses an obstacle in automatic dictation systems as speech recognition output needs to undergo a fair amount of editing in order to turn it into a document that complies with the customary standards. We present an approach that attempts to perform this edit from recognized words to final document automatically by learning the appropriate transformations from example documents. ...

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  • Grounded language models represent the relationship between words and the non-linguistic context in which they are said. This paper describes how they are learned from large corpora of unlabeled video, and are applied to the task of automatic speech recognition of sports video. Results show that grounded language models improve perplexity and word error rate over text based language models, and further, support video information retrieval better than human generated speech transcriptions.

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  • While speech recognition systems have come a long way in the last thirty years, there is still room for improvement. Although readily available, these systems are sometimes inaccurate and insufficient. The research presented here outlines a technique called Distributed Listening which demonstrates noticeable improvements to existing speech recognition methods. The Distributed Listening architecture introduces the idea of multiple, parallel, yet physically separate automatic speech recognizers called listeners. Distributed Listening also uses a piece of middleware called an interpreter.

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  • Acquisition of prosody, in addition to vocabulary and grammar, is essential for language learners. However, it has received less attention in instruction. To enable automatic identification and feedback on learners’ prosodic errors, we investigate automatic pitch accent labeling for nonnative speech. We demonstrate that an acoustic-based context model can achieve accuracies over 79% on binary pitch accent recognition when trained on withingroup data.

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  • This paper presents three methods that can be used to recognize paraphrases. They all employ string similarity measures applied to shallow abstractions of the input sentences, and a Maximum Entropy classifier to learn how to combine the resulting features. Two of the methods also exploit WordNet to detect synonyms and one of them also exploits a dependency parser. We experiment on two datasets, the MSR paraphrasing corpus and a dataset that we automatically created from the MTC corpus. Our system achieves state of the art or better results. ...

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