Research Status and Future Development of Endpoint Detection Algorithms Based on Computer Science Language Signals
Today is an information technology era, language signal recognition technology, language signal coding technology and a variety of new language technology will be widely used in all areas of our life, such as: security field, human-computer interaction, communication field. We can accurately analyze...
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| Vydané v: | Journal of physics. Conference series Ročník 1744; číslo 3; s. 32128 - 32132 |
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| Jazyk: | English |
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IOP Publishing
01.02.2021
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | Today is an information technology era, language signal recognition technology, language signal coding technology and a variety of new language technology will be widely used in all areas of our life, such as: security field, human-computer interaction, communication field. We can accurately analyze pure speech signals and silent segments in a speech by using language signal endpoint detection technology, which will bring decisive effects to the efficiency of ASR and ASC. Three steps can be used to represent the language endpoint detection model, namely, the language signal preprocessing step, the extraction of the characteristic vector of the whole language flow, and the establishment of the language endpoint discrimination model. Finally, the speech endpoint discrimination model is established. The traditional speech endpoint detection algorithms include the two threshold method based on time domain, the universal entropy method based on frequency domain and the invert characteristic method. Aiming at the low SNR and complex noise environment, in order to obtain satisfactory endpoint detection effect, this paper proposes an endpoint detection model based on optimized Extreme Learning machine (ELM), and makes up for the deficiencies of the algorithm itself by optimizing network connection parameters[1-2]. |
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| AbstractList | Today is an information technology era, language signal recognition technology, language signal coding technology and a variety of new language technology will be widely used in all areas of our life, such as: security field, human-computer interaction, communication field. We can accurately analyze pure speech signals and silent segments in a speech by using language signal endpoint detection technology, which will bring decisive effects to the efficiency of ASR and ASC. Three steps can be used to represent the language endpoint detection model, namely, the language signal preprocessing step, the extraction of the characteristic vector of the whole language flow, and the establishment of the language endpoint discrimination model. Finally, the speech endpoint discrimination model is established. The traditional speech endpoint detection algorithms include the two threshold method based on time domain, the universal entropy method based on frequency domain and the invert characteristic method. Aiming at the low SNR and complex noise environment, in order to obtain satisfactory endpoint detection effect, this paper proposes an endpoint detection model based on optimized Extreme Learning machine (ELM), and makes up for the deficiencies of the algorithm itself by optimizing network connection parameters[1-2]. Today is an information technology era, language signal recognition technology, language signal coding technology and a variety of new language technology will be widely used in all areas of our life, such as: security field, human-computer interaction, communication field. We can accurately analyze pure speech signals and silent segments in a speech by using language signal endpoint detection technology, which will bring decisive effects to the efficiency of ASR and ASC. Three steps can be used to represent the language endpoint detection model, namely, the language signal preprocessing step, the extraction of the characteristic vector of the whole language flow, and the establishment of the language endpoint discrimination model. Finally, the speech endpoint discrimination model is established. The traditional speech endpoint detection algorithms include the two threshold method based on time domain, the universal entropy method based on frequency domain and the invert characteristic method. Aiming at the low SNR and complex noise environment, in order to obtain satisfactory endpoint detection effect, this paper proposes an endpoint detection model based on optimized Extreme Learning machine (ELM), and makes up for the deficiencies of the algorithm itself by optimizing network connection parameters [1-2] . |
| Author | Wang, Juan |
| Author_xml | – sequence: 1 givenname: Juan surname: Wang fullname: Wang, Juan email: wangjuan@llhc.edu.cn organization: LUliang University, Computer Science and Technology , China |
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| CitedBy_id | crossref_primary_10_4018_IJCINI_386839 |
| Cites_doi | 10.1109/TSP.2006.874403 10.4028/www.scientific.net/AMM.596.723 |
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| References | Chang (JPCS_1744_3_032128bib8) 2006; 54 Fang (JPCS_1744_3_032128bib6) 2015; 40 Lee (JPCS_1744_3_032128bib2) 2014; 36 Wang (JPCS_1744_3_032128bib7) 2014; 50 He (JPCS_1744_3_032128bib3) 2013; 33 Zhang (JPCS_1744_3_032128bib1) 2014 Lu (JPCS_1744_3_032128bib9) 2014; 34 Li (JPCS_1744_3_032128bib5) 2013; 49 Baisen (JPCS_1744_3_032128bib4) 2013 Pal (JPCS_1744_3_032128bib10) 2015 |
| References_xml | – start-page: 116 year: 2013 ident: JPCS_1744_3_032128bib4 article-title: Speech Endpoint detection with Low SNR Based or HHTSM [C] – volume: 40 start-page: 296 year: 2015 ident: JPCS_1744_3_032128bib6 article-title: Chlorophyll inversion of mixed vegetation based on continuous wavelet analysis publication-title: Journal of Wuhan University (Information Science) – start-page: 192 year: 2014 ident: JPCS_1744_3_032128bib1 article-title: speech endpoint detection integrating Burg spectrum estimation and signal change rate measure [J] publication-title: Journal of Xidian University (Natural Science) – volume: 54 start-page: 1965 year: 2006 ident: JPCS_1744_3_032128bib8 article-title: Oice Activity Detection Based on Multiple Statistical Models [J] publication-title: Signal Procesing, IEEE Transactions on doi: 10.1109/TSP.2006.874403 – start-page: 381 year: 2015 ident: JPCS_1744_3_032128bib10 article-title: Modified energy based method for word endpoints detection of continuous speech signal in real world environment [C] – volume: 49 start-page: 144 year: 2013 ident: JPCS_1744_3_032128bib5 article-title: short-time TEO energy application in Speech Endpoint Detection with Noise [J] publication-title: Computer Engineering and Applications – volume: 34 start-page: 1386 year: 2014 ident: JPCS_1744_3_032128bib9 article-title: Improved speech endpoint detection algorithm under strong noise environment [J] publication-title: Computer applications – volume: 33 year: 2013 ident: JPCS_1744_3_032128bib3 article-title: speech endpoint detection Based on critical band and energy entropy [J] publication-title: Computer applications – volume: 36 start-page: 714 year: 2014 ident: JPCS_1744_3_032128bib2 article-title: Weighed - Finite State Transduer - based Endpoint Detection Using Probabilistic Decision Logic [J] publication-title: J ETRIJOURNAL – volume: 50 start-page: 723 year: 2014 ident: JPCS_1744_3_032128bib7 article-title: The Research of Speech Recognition in Low SNR Based on GA-SVM [J] publication-title: Applied Mechanics and Materials doi: 10.4028/www.scientific.net/AMM.596.723 |
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| SubjectTerms | Language Flow Preprocessing Speech Endpoint Detection Speech Recognition |
| Title | Research Status and Future Development of Endpoint Detection Algorithms Based on Computer Science Language Signals |
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