Relevant bibliographies by topics / Speech processing systems; Speech synthesis

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  2. Dissertations / Theses
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Academic literature on the topic 'Speech processing systems; Speech synthesis'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Speech processing systems; Speech synthesis"

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Järvinen, Kari. "Digital speech processing: Speech coding, synthesis, and recognition." Signal Processing 30, no. 1 (1993): 133–34. http://dx.doi.org/10.1016/0165-1684(93)90056-g.

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M Tasbolatov, N. Mekebayev, O. Mamyrbayev, M. Turdalyuly, D. Oralbekova,. "Algorithms and architectures of speech recognition systems." Psychology and Education Journal 58, no. 2 (2021): 6497–501. http://dx.doi.org/10.17762/pae.v58i2.3182.

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Digital processing of speech signal and the voice recognition algorithm is very important for fast and accurate automatic scoring of the recognition technology. A voice is a signal of infinite information. The direct analysis and synthesis of a complex speech signal is due to the fact that the information is contained in the signal.
 Speech is the most natural way of communicating people. The task of speech recognition is to convert speech into a sequence of words using a computer program.
 This article presents an algorithm of extracting MFCC for speech recognition. The MFCC algorithm reduces the processing power by 53% compared to the conventional algorithm. Automatic speech recognition using Matlab.
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Delic, Vlado, Darko Pekar, Radovan Obradovic, and Milan Secujski. "Speech signal processing in ASR&TTS algorithms." Facta universitatis - series: Electronics and Energetics 16, no. 3 (2003): 355–64. http://dx.doi.org/10.2298/fuee0303355d.

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Speech signal processing and modeling in systems for continuous speech recognition and Text-to-Speech synthesis in Serbian language are described in this paper. Both systems are fully developed by the authors and do not use any third party software. Accuracy of the speech recognizer and intelligibility of the TTS system are in the range of the best solutions in the world, and all conditions are met for commercial use of these solutions.
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Varga, A., and F. Fallside. "A technique for using multipulse linear predictive speech synthesis in text-to-speech type systems." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 4 (1987): 586–87. http://dx.doi.org/10.1109/tassp.1987.1165151.

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Delić, Vlado, Zoran Perić, Milan Sečujski, et al. "Speech Technology Progress Based on New Machine Learning Paradigm." Computational Intelligence and Neuroscience 2019 (June 25, 2019): 1–19. http://dx.doi.org/10.1155/2019/4368036.

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Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems. Additionally, the article discusses the concepts and recent advances in speech signal compression, coding, and transmission, including cognitive speech coding. To conclude, the main intention of this article is to highlight recent achievements and challenges based on new machine learning paradigms that, over the last decade, had an immense impact in the field of speech signal processing.
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Sunitha, Dr K. V. N., and P. Sunitha Devi. "Text Normalization for Telugu Text-to-Speech Synthesis." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 2 (2013): 2241–49. http://dx.doi.org/10.24297/ijct.v11i2.1176.

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Most areas related to language and speech technology, directly or indirectly, require handling of unrestricted text, and Text-to-speech systems directly need to work on real text. To build a natural sounding speech synthesis system, it is essential that the text processing component produce an appropriate sequence of phonemic units corresponding to an arbitrary input text. A novel approach is used, where the input text is tokenized, and classification is done based on token type. The token sense disambiguation is achieved by the semantic nature of the language and then the expansion rules are applied to get the normalized text. However, for Telugu language not much work is done on text normalization. In this paper we discuss our efforts for designing a rule based system to achieve text normalization in the context of building Telugu text-to-speech system.
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Reddy, Bharathi, D. Leela Rani, and Prof S. Varadarajan. "HIGH SPEED CARRY SAVE MULTIPLIER BASED LINEAR CONVOLUTION USING VEDIC MATHAMATICS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (2013): 284–87. http://dx.doi.org/10.24297/ijct.v4i2a2.3173.

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VLSI applications include Digital Signal Processing, Digital control systems, Telecommunications, Speech and Audio processing for audiology and speech language pathology. The latest research in VLSI is the design and implementation of DSP systems which are essential for above applications. The fundamental computation in DSP Systems is convolution. Convolution and LTI systems are the heart and soul of DSP. The behavior of LTI systems in continuous time is described by Convolution integral whereas the behavior in discrete-time is described by Linear convolution. In this paper, Linear convolution is performed using carry save multiplier architecture based on vertical and cross wise algorithm of Urdhva – Tiryagbhyam in Vedic mathematics. Coding is done using Verilog HDL(verilog Hardware Description Language). Simulation and Synthesis are performed using Xilinx FPGA
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Chabchoub, Abdelkader, and Adnan Cherif. "Implementation of the Arabic Speech Synthesis with TD-PSOLA Modifier." International Journal of Signal System Control and Engineering Application 3, no. 4 (2010): 77–80. http://dx.doi.org/10.3923/ijssceapp.2010.77.80.

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Modi, Rohan. "Transcript Anatomization with Multi-Linguistic and Speech Synthesis Features." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1755–58. http://dx.doi.org/10.22214/ijraset.2021.35371.

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Handwriting Detection is a process or potential of a computer program to collect and analyze comprehensible input that is written by hand from various types of media such as photographs, newspapers, paper reports etc. Handwritten Text Recognition is a sub-discipline of Pattern Recognition. Pattern Recognition is refers to the classification of datasets or objects into various categories or classes. Handwriting Recognition is the process of transforming a handwritten text in a specific language into its digitally expressible script represented by a set of icons known as letters or characters. Speech synthesis is the artificial production of human speech using Machine Learning based software and audio output based computer hardware. While there are many systems which convert normal language text in to speech, the aim of this paper is to study Optical Character Recognition with speech synthesis technology and to develop a cost effective user friendly image based offline text to speech conversion system using CRNN neural networks model and Hidden Markov Model. The automated interpretation of text that has been written by hand can be very useful in various instances where processing of great amounts of handwritten data is required, such as signature verification, analysis of various types of documents and recognition of amounts written on bank cheques by hand.
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Thoidis, Iordanis, Lazaros Vrysis, Dimitrios Markou, and George Papanikolaou. "Temporal Auditory Coding Features for Causal Speech Enhancement." Electronics 9, no. 10 (2020): 1698. http://dx.doi.org/10.3390/electronics9101698.

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Perceptually motivated audio signal processing and feature extraction have played a key role in the determination of high-level semantic processes and the development of emerging systems and applications, such as mobile phone telecommunication and hearing aids. In the era of deep learning, speech enhancement methods based on neural networks have seen great success, mainly operating on the log-power spectra. Although these approaches surpass the need for exhaustive feature extraction and selection, it is still unclear whether they target the important sound characteristics related to speech perception. In this study, we propose a novel set of auditory-motivated features for single-channel speech enhancement by fusing temporal envelope and temporal fine structure information in the context of vocoder-like processing. A causal gated recurrent unit (GRU) neural network is employed to recover the low-frequency amplitude modulations of speech. Experimental results indicate that the exploited system achieves considerable gains for normal-hearing and hearing-impaired listeners, in terms of objective intelligibility and quality metrics. The proposed auditory-motivated feature set achieved better objective intelligibility results compared to the conventional log-magnitude spectrogram features, while mixed results were observed for simulated listeners with hearing loss. Finally, we demonstrate that the proposed analysis/synthesis framework provides satisfactory reconstruction accuracy of speech signals.
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Dissertations / Theses on the topic "Speech processing systems; Speech synthesis"

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Liu, Zhu Lin. "Speech synthesis via adaptive Fourier decomposition." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493215.

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Mazel, David S. "Sinusoidal modeling of speech." Thesis, Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/13873.

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Macon, Michael W. "Speech synthesis based on sinusoidal modeling." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13904.

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Chung, Jae H. "A new homomorphic vocoder framework using analysis-by-synthesis excitation analysis." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/15471.

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Crosmer, Joel R. "Very low bit rate speech coding using the line spectrum pair transformation of the LPC coefficients." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/15739.

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Cummings, Kathleen E. "Analysis, synthesis, and recognition of stressed speech." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/15673.

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Farges, Eric P. "An analysis-synthesis hidden Markov model of speech." Diss., Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/14775.

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Rose, Richard C. "The design and performance of an analysis-by-synthesis class of predictive speech coders." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/16693.

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Peters, Richard Alan II. "A LINEAR PREDICTION CODING MODEL OF SPEECH (SYNTHESIS, LPC, COMPUTER, ELECTRONIC)." Thesis, The University of Arizona, 1985. http://hdl.handle.net/10150/291240.

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Lam, Victor T. M. "The stability of pitch synthesis filters in speech coding /." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63361.

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Books on the topic "Speech processing systems; Speech synthesis"

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Speech processing and synthesis toolboxes. Wiley, 2000.

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İnce, A. Nejat. Digital Speech Processing: Speech Coding, Synthesis and Recognition. Springer US, 1992.

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Voice and speech processing. McGraw-Hill, 1987.

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Yannakoudakis, E. J. Speech synthesis and recognition systems. E. Horwood, 1987.

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1932-, Fallside Frank, and Woods William A, eds. Computer speech processing. Prentice-Hall International, 1985.

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Furui, Sadaoki. Digital speech processing, synthesis, and recognition. Marcel Dekker, 1989.

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Digital speech processing, synthesis, and recognition. 2nd ed. Marcel Dekker, 2001.

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Taylor, Paul. Text-to-speech synthesis. Cambridge University Press, 2009.

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Katshuhiko, Shirai. Recent progress in Japanese speech synthesis. Gordon and Breach Science Publishers, 2000.

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United States. Social Security Administration. Technology Assessment and Forecasting Group. ADP voice technology: Speech recognition and speech synthesis. U.S. Social Security Administration, 1985.

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Book chapters on the topic "Speech processing systems; Speech synthesis"

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Sinha, Priyabrata. "Speech Synthesis." In Speech Processing in Embedded Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_11.

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Ohala, John J. "Linguistics and Automatic Processing of Speech." In New Systems and Architectures for Automatic Speech Recognition and Synthesis. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82447-0_18.

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Cytowski, Jerzy. "Speech Understanding System SUSY—A New Version of the Speech Synthesis Program." In Aspects of Natural Language Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04735-0_12.

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Kunt, Murat, and Heinz Hugli. "An Overview of Digital Techniques for Processing Speech Signals." In New Systems and Architectures for Automatic Speech Recognition and Synthesis. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82447-0_1.

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Liu, Jianli, and Jinying Chen. "The Application of Speech Synthesis in Car Warning System." In The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00536-2_75.

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Lawless, Séamus, Peter Lavin, Mostafa Bayomi, João P. Cabral, and M. Rami Ghorab. "Text Summarization and Speech Synthesis for the Automated Generation of Personalized Audio Presentations." In Natural Language Processing and Information Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19581-0_28.

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Ruske, G. "Demisyllables as Processing Units for Automatic Speech Recognition and Lexical Access." In New Systems and Architectures for Automatic Speech Recognition and Synthesis. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82447-0_24.

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Yoon, Kyuchul. "A Prosodic Diphone Database for Korean Text-to-Speech Synthesis System." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_45.

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Datta, Asoke Kumar, and Arup Saha. "A System for Analysis of Large Scale Speech Data for the Development of Rules of Intonation for Speech Synthesis." In Speech, Sound and Music Processing: Embracing Research in India. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31980-8_15.

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Owens, F. J. "Speech Synthesis." In Signal Processing of Speech. Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-22599-6_5.

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Conference papers on the topic "Speech processing systems; Speech synthesis"

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Rybarova, Renata, Ivan Drozd, and Gregor Rozinaj. "GUI for interactive speech synthesis." In 2016 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2016. http://dx.doi.org/10.1109/iwssip.2016.7502776.

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Tabet, Youcef, and Mohamed Boughazi. "Speech synthesis techniques. A survey." In 2011 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA). IEEE, 2011. http://dx.doi.org/10.1109/wosspa.2011.5931414.

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Kohata, Minoru. "Secure Speech Encryption System Using Segments for Speech Synthesis." In 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2014. http://dx.doi.org/10.1109/iih-msp.2014.72.

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Silva, Samuel, and António Teixeira. "An Anthropomorphic Perspective for Audiovisual Speech Synthesis." In 10th International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006150201630172.

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Hashimoto, Kei, Junichi Yamagishi, William Byrne, Simon King, and Keiichi Tokuda. "An analysis of machine translation and speech synthesis in speech-to-speech translation system." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947506.

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Lu, Heng, Zhen-Hua Ling, Li-Rong Dai, and Ren-Hua Wang. "Building HMM based unit-selection speech synthesis system using synthetic speech naturalness evaluation score." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947567.

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Akagi, Masato, Xiao Han, Reda Elbarougy, Yasuhiro Hamada, and Junfeng Li. "Emotional Speech Recognition and Synthesis in Multiple Languages toward Affective Speech-to-Speech Translation System." In 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2014. http://dx.doi.org/10.1109/iih-msp.2014.148.

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Song, Eunwoo, Jin-Seob Kim, Kyungguen Byun, and Hong-Goo Kang. "Speaker-Adaptive Neural Vocoders for Parametric Speech Synthesis Systems." In 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2020. http://dx.doi.org/10.1109/mmsp48831.2020.9287168.

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Reddy, M. Gurunath, D. M. Harikrishna, K. Sreenivasa Rao, and K. E. Manjunath. "Telugu emotional story speech synthesis using SABLE markup language." In 2015 International Conference on Signal Processing And Communication Engineering Systems (SPACES). IEEE, 2015. http://dx.doi.org/10.1109/spaces.2015.7058278.

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Ananthakrishnan, Sankaranarayanan, Stavros Tsakalidis, Rohit Prasad, Prem Natarajan, and Aravind Namandi Vembu. "Automatic pronunciation prediction for text-to-speech synthesis of dialectal arabic in a speech-to-speech translation system." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6289032.

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Reports on the topic "Speech processing systems; Speech synthesis"

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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.
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