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Towards a part-of-speech tagger for Sranan Tongo ...
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Nicolás, C.V.; Viktor, Z.. - : Фонд содействия развитию интернет-медиа, ИТ-образования, человеческого потенциала "Лига интернет-медиа", 2022
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Human Gait Phase Recognition in Embedded Sensor System
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Liu, Zhenbang. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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Automatische Gebärdenspracherkennung: Von Videokorpora zu Glossensätzen ... : Automatic sign language recognition : from video corpora to gloss sentences ...
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Comparison of Machine Learning Models: Gesture Recognition Using a Multimodal Wrist Orthosis for Tetraplegics
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In: The Journal of Purdue Undergraduate Research (2020)
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Subunits Inference and Lexicon Development Based on Pairwise Comparison of Utterances and Signs
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In: Information ; Volume 10 ; Issue 10 (2019)
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Abstract:
Communication languages convey information through the use of a set of symbols or units. Typically, this unit is word. When developing language technologies, as words in a language do not have the same prior probability, there may not be sufficient training data for each word to model. Furthermore, the training data may not cover all possible words in the language. Due to these data sparsity and word unit coverage issues, language technologies employ modeling of subword units or subunits, which are based on prior linguistic knowledge. For instance, development of speech technologies such as automatic speech recognition system presume that there exists a phonetic dictionary or at least a writing system for the target language. Such knowledge is not available for all languages in the world. In that direction, this article develops a hidden Markov model-based abstract methodology to extract subword units given only pairwise comparison between utterances (or realizations of words in the mode of communication), i.e., whether two utterances correspond to the same word or not. We validate the proposed methodology through investigations on spoken language and sign language. In the case of spoken language, we demonstrate that the proposed methodology can lead up to discovery of phone set and development of phonetic dictionary. In the case of sign language, we demonstrate how hand movement information can be effectively modeled for sign language processing and synthesized back to gain insight about the derived subunits.
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Keyword:
hidden Markov model; phone set; pronunciation lexicon; sign language processing; speech processing; subword units; under-resourced
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URL: https://doi.org/10.3390/info10100298
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Acoustic event, spoken keyword and emotional outburst detection
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Prototyputveckling för skalbar motor med förståelse för naturligt språk ; Prototype development for a scalable engine with natural language understanding
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Automatic assessment of singing voice pronunciation: a case study with Jingju music
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In: TDX (Tesis Doctorals en Xarxa) (2018)
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Detecting sections and entities in court decisions using HMM and CRF graphical models
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In: Conférence Extraction et Gestion des Connaissances ; https://hal.archives-ouvertes.fr/hal-02101479 ; Conférence Extraction et Gestion des Connaissances, Université Grenoble alpes (UGA), Jan 2017, Grenoble, France ; http://egc2017.imag.fr/ (2017)
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Score-Informed Syllable Segmentation For Jingju A Cappella Singing Voice With Mel-Frequency Intensity Profiles ...
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Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
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RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
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In: Publications (2017)
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Stem-based PoS tagging for agglutinative languages ; Sondan Eklemeli Dillerde Gövde Tabanlı Sözcük Türü ˙I¸saretleme
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О ВОЗМОЖНОСТИ МАТЕМАТИЧЕСКОГО МОДЕЛИРОВАНИЯ ЭВОЛЮЦИИ ПОЛИСЕМИИ ЗНАКОВ ЕСТЕСТВЕННОГО ЯЗЫКА С ПОМОЩЬЮ НЕСТАЦИОНАРНЫХ ПРОЦЕССОВ РОЖДЕНИЯ И ГИБЕЛИ
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ПОДДУБНЫЙ ВАСИЛИЙ ВАСИЛЬЕВИЧ. - : Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования «Национальный исследовательский Томский государственный университет», 2016
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