Bibliographies thématiques / Subtitling, Speech Translation, Neural Machine Translation, Evaluation

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Littérature scientifique sur le sujet « Subtitling, Speech Translation, Neural Machine Translation, Evaluation »

Auteur : Grafiati
Publié le 11 mars 2023

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Articles de revues sur le sujet "Subtitling, Speech Translation, Neural Machine Translation, Evaluation"

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Yan, Li. "Real-Time Automatic Translation Algorithm for Chinese Subtitles in Media Playback Using Knowledge Base." Mobile Information Systems 2022 (June 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/5245035.

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Currently, speech technology allows for simultaneous subtitling of live television programs using speech recognition and the respeaking approach. Although many previous studies on the quality of live subtitling utilizing voice recognition have been proposed, little attention has been paid to the quantitative elements of subtitles. Due to the high performance of neural machine translation (NMT), it has become the standard machine translation method. A data-driven translation approach requires high-quality, large-scale training data and powerful computing resources to achieve good performance. H
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Narayan, Ravi, V. P. Singh, and S. Chakraverty. "Quantum Neural Network Based Machine Translator for Hindi to English." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/485737.

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This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and eva
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Ganesh, Preetham, Bharat S. Rawal, Alexander Peter, and Andi Giri. "POS-Tagging based Neural Machine Translation System for European Languages using Transformers." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 18 (May 24, 2021): 26–33. http://dx.doi.org/10.37394/23209.2021.18.5.

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The interaction between human beings has always faced different kinds of difficulties. One of those difficulties is the language barrier. It would be a tedious task for someone to learn all the syllables in a new language in a short period and converse with a native speaker without grammatical errors. Moreover, having a language translator at all times would be intrusive and expensive. We propose a novel approach to Neural Machine Translation (NMT) system using interlanguage word similaritybased model training and Part-Of-Speech (POS) Tagging based model testing. We compare these approaches us
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Belinkov, Yonatan, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. "On the Linguistic Representational Power of Neural Machine Translation Models." Computational Linguistics 46, no. 1 (2020): 1–52. http://dx.doi.org/10.1162/coli_a_00367.

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Despite the recent success of deep neural networks in natural language processing and other spheres of artificial intelligence, their interpretability remains a challenge. We analyze the representations learned by neural machine translation (NMT) models at various levels of granularity and evaluate their quality through relevant extrinsic properties. In particular, we seek answers to the following questions: (i) How accurately is word structure captured within the learned representations, which is an important aspect in translating morphologically rich languages? (ii) Do the representations ca
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Arora, Karunesh Kumar, and Shyam Sunder Agrawal. "Source-side Reordering to Improve Machine Translation between Languages with Distinct Word Orders." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (2021): 1–18. http://dx.doi.org/10.1145/3448252.

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English and Hindi have significantly different word orders. English follows the subject-verb-object (SVO) order, while Hindi primarily follows the subject-object-verb (SOV) order. This difference poses challenges to modeling this pair of languages for translation. In phrase-based translation systems, word reordering is governed by the language model, the phrase table, and reordering models. Reordering in such systems is generally achieved during decoding by transposing words within a defined window. These systems can handle local reorderings, and while some phrase-level reorderings are carried
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P., Dr Karrupusamy. "Analysis of Neural Network Based Language Modeling." March 2020 2, no. 1 (2020): 53–63. http://dx.doi.org/10.36548/jaicn.2020.1.006.

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The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, ps
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P., Dr Karrupusamy. "Analysis of Neural Network Based Language Modeling." March 2020 2, no. 1 (2020): 53–63. http://dx.doi.org/10.36548/jaicn.2020.3.006.

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The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, ps
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Xue, Nan. "Analysis Model of Spoken English Evaluation Algorithm Based on Intelligent Algorithm of Internet of Things." Computational Intelligence and Neuroscience 2022 (March 27, 2022): 1–8. http://dx.doi.org/10.1155/2022/8469945.

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With the in-depth promotion of the national strategy for the integration of artificial intelligence technology and entity development, speech recognition processing technology, as an important medium of human-computer interaction, has received extensive attention and motivated research in industry and academia. However, the existing accurate speech recognition products are based on massive data platform, which has the problems of slow response and security risk, which makes it difficult for the existing speech recognition products to meet the application requirements for timely translation of
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Wu, Long, Ta Li, Li Wang, and Yonghong Yan. "Improving Hybrid CTC/Attention Architecture with Time-Restricted Self-Attention CTC for End-to-End Speech Recognition." Applied Sciences 9, no. 21 (2019): 4639. http://dx.doi.org/10.3390/app9214639.

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As demonstrated in hybrid connectionist temporal classification (CTC)/Attention architecture, joint training with a CTC objective is very effective to solve the misalignment problem existing in the attention-based end-to-end automatic speech recognition (ASR) framework. However, the CTC output relies only on the current input, which leads to the hard alignment issue. To address this problem, this paper proposes the time-restricted attention CTC/Attention architecture, which integrates an attention mechanism with the CTC branch. “Time-restricted” means that the attention mechanism is conducted
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Karakanta, Alina. "Experimental research in automatic subtitling." Translation Spaces, June 14, 2022. http://dx.doi.org/10.1075/ts.21021.kar.

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Abstract Recent developments in neural machine translation, and especially speech translation, are gradually but firmly entering the field of audiovisual translation (AVT). Automation in subtitling is extending from a machine translation (MT) component to fully automatic subtitling, which comprises MT, auto-spotting and automatic segmentation. The rise of this new paradigm renders MT-oriented experimental designs inadequate for the evaluation and investigation of automatic subtitling, since they fail to encompass the multimodal nature and technical requirements of subtitling. This paper highli
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Thèses sur le sujet "Subtitling, Speech Translation, Neural Machine Translation, Evaluation"

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Karakanta, Alina. "Automatic subtitling: A new paradigm." Doctoral thesis, Università degli studi di Trento, 2022. https://hdl.handle.net/11572/356701.

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Audiovisual Translation (AVT) is a field where Machine Translation (MT) has long found limited success mainly due to the multimodal nature of the source and the formal requirements of the target text. Subtitling is the predominant AVT type, quickly and easily providing access to the vast amounts of audiovisual content becoming available daily. Automation in subtitling has so far focused on MT systems which translate source language subtitles, already transcribed and timed by humans. With recent developments in speech translation (ST), the time is ripe for extended automation in subtitling, wit
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Actes de conférences sur le sujet "Subtitling, Speech Translation, Neural Machine Translation, Evaluation"

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Primandhika, Restu Bias, Muhammad Nadzeri Munawar, and Aceng Ruhendi Saifullah. "Experiment on a Transformer Model Indonesian-to-Sundanese Neural Machine Translation with Sundanese Speech Level Evaluation." In Thirteenth Conference on Applied Linguistics (CONAPLIN 2020). Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210427.069.

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