Relevant bibliographies by topics / Speech processing

Academic literature on the topic 'Speech processing'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Speech processing"

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Yoo, Hah-Young. "Method for processing speech signal in speech processing system." Journal of the Acoustical Society of America 103, no. 4 (April 1998): 1699. http://dx.doi.org/10.1121/1.421327.

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Tato, Raquel. "Pre-processing speech for speech recognition." Journal of the Acoustical Society of America 128, no. 2 (2010): 964. http://dx.doi.org/10.1121/1.3481751.

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Tsuhan Chen. "Audiovisual speech processing." IEEE Signal Processing Magazine 18, no. 1 (2001): 9–21. http://dx.doi.org/10.1109/79.911195.

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Perdereau, Xiao. "Speech rhythm processing." Journal of the Acoustical Society of America 137, no. 4 (April 2015): 2272. http://dx.doi.org/10.1121/1.4920299.

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Jarvinen, Kari. "Speech code processing." Journal of the Acoustical Society of America 101, no. 4 (April 1997): 1765. http://dx.doi.org/10.1121/1.418197.

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Garner, Philip Neil, and Jason Peter Andrew Charlesworth. "Speech processing system." Journal of the Acoustical Society of America 118, no. 1 (2005): 29. http://dx.doi.org/10.1121/1.1999431.

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Taguchi, Tetsu. "Speech processing system." Journal of the Acoustical Society of America 95, no. 1 (January 1994): 590. http://dx.doi.org/10.1121/1.408275.

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Miyamae, Koichi, and Satoshi Omata. "Speech processing apparatus." Journal of the Acoustical Society of America 95, no. 1 (January 1994): 591–92. http://dx.doi.org/10.1121/1.408282.

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Rivenez, Marie, Christopher J. Darwin, and Anne Guillaume. "Processing unattended speech." Journal of the Acoustical Society of America 119, no. 6 (June 2006): 4027–40. http://dx.doi.org/10.1121/1.2190162.

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Rajan, Jebu Jacob. "Speech processing system." Journal of the Acoustical Society of America 121, no. 1 (2007): 28. http://dx.doi.org/10.1121/1.2434326.

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Dissertations / Theses on the topic "Speech processing"

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Xu, Jue. "Adaptations in Speech Processing." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/23030.

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Wie sich die Sprachwahrnehmung an ständig eingehende Informationen anpasst, ist eine Schlüsselfrage in der Gedanken- und Gehirnforschung. Die vorliegende Dissertation zielt darauf ab, zum Verständnis von Anpassungen an die Sprecheridentität und Sprachfehler während der Sprachverarbeitung beizutragen und unser Wissen über die Rolle der kognitiven Kontrolle bei der Sprachverarbeitung zu erweitern. Zu diesem Zweck wurden ereigniskorrelierte Potentiale (EKPs, englisch: event-related potentials, ERPs) N400 und P600 in der Elektroenzephalographie (EEG) analysiert. Die vorliegende Arbeit befasste sich insbesondere mit der Frage nach der Anpassung an die Sprecheridentität bei der Verarbeitung von zwei Arten von Sprachfehlern (Xu, Abdel Rahman, & Sommer, 2019), und untersuchte die proaktive Anpassungen, die durch die Erkennung von Sprachfehlern (Xu, Abdel Rahman, & Sommer, 2021) und durch die Sprecher(dis)kontinuität über aufeinanderfolgende Sätze in Situationen mit mehreren Sprechern ausgelöst wurden (Xu, Abdel Rahman, & Sommer, 2021, in press). Die Ergebnisse zeigten, dass unterschiedliche Sprachverarbeitungsstrategien entsprechend der Sprecheridentität von Muttersprachlern oder Nicht-Muttersprachlern und zwei verschiedenen Arten von Sprachfehlern angepasst wurden, was sich in unterschiedlichen N400- und P600-Effekten widerspiegelte. Darüber hinaus kann die Erkennung von Konflikten (Sprachfehler) und Sprecher(dis)kontinuität über aufeinanderfolgende Sätze hinweg eine proaktive kognitive Kontrolle erfordern, die die Verarbeitungsstrategien für den folgenden Satz schnell anpasst, was sich in bisher nicht gemeldeten sequentiellen Anpassungseffekten in der P600-Amplitude manifestierte. Basierend auf dem DMC Modell (Braver, 2012; Braver, Gray, & Burgess, 2007) und dem Überwachungsmodell der Sprachverarbeitung (van de Meerendonk, Indefrey, Chwilla, & Kolk, 2011) schlage ich vor, dass die P600-Amplitude nicht nur reaktive Anpassungen manifestiert, die durch Konflikterkennung ausgelöst werden, nämlich die klassischen P600-Effekte, die eine erneute Analyse der Sprachverarbeitung widerspiegeln, sondern auch proaktive Anpassungen in der Überwachung der Sprachverarbeitung, die Mechanismen der kognitiven Kontrolle von Aufmerksamkeit und Gedächtnis beinhalten.
How language perception adapts to constantly incoming information is a key question in mind and brain research. This doctoral thesis aims to contribute to the understanding of adaptation to speaker identity and speech error during speech processing, and to enhance our knowledge about the role of cognitive control in speech processing. For this purpose, event-related brain potentials (ERPs) N400 and P600 in the electroencephalography (EEG) were analyzed. Specifically, the present work addressed the question about adaptation to the speaker’s identity in processing two types of speech errors (Xu, Abdel Rahman, & Sommer, 2019), and explored proactive adaptation initiated by the detection of speech errors (Xu, Abdel Rahman, & Sommer, 2021) and by speaker (dis-)continuity across consecutive sentences in multi-speaker situations (Xu, Abdel Rahman, & Sommer, 2021, in press). Results showed that different speech processing strategies were adapted according to native or non-native speaker identity and two different types of speech errors, reflected in different N400 and P600 effects. In addition, detection of conflict (speech error) and speaker (dis-)continuity across consecutive sentences engage cognitive control to rapidly adapt processing strategies for the following sentence, manifested in hitherto unreported sequential adaptation effects in the P600 amplitude. Based on the DMC model (Braver, 2012; Braver, Gray, & Burgess, 2007) and the monitoring theory of language perception (van de Meerendonk, Indefrey, Chwilla, & Kolk, 2011), I propose that the P600 amplitude manifests not only reactive adaptations triggered by conflict detection, i.e., the classic P600 effect, reflecting reanalysis of speech processing, but also proactive adaptations in monitoring the speech processing, engaging cognitive control mechanisms of attention and memory.
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Thomas, Mark R. P. "Glottal-synchronous speech processing." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/5611.

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Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity of voiced speech is exploited. Traditionally, speech processing involves segmenting and processing short speech frames of predefined length; this may fail to exploit the inherent periodic structure of voiced speech which glottal-synchronous speech frames have the potential to harness. Glottal-synchronous frames are often derived from the glottal closure instants (GCIs) and glottal opening instants (GOIs). The SIGMA algorithm was developed for the detection of GCIs and GOIs from the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and GOI detection from speech signals, the YAGA algorithm provides a measured accuracy of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to reverberation than single-channel algorithms. The GCIs are applied to real-world applications including speech dereverberation, where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance of voicing detection in glottal-synchronous algorithms is demonstrated by subjective testing. The GCIs are further exploited in a new area of data-driven speech modelling, providing new insights into speech production and a set of tools to aid deployment into real-world applications. The technique is shown to be applicable in areas of speech coding, identification and artificial bandwidth extension of telephone speech
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Lucey, Simon. "Audio-visual speech processing." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36172/7/SimonLuceyPhDThesis.pdf.

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Speech is inherently bimodal, relying on cues from the acoustic and visual speech modalities for perception. The McGurk effect demonstrates that when humans are presented with conflicting acoustic and visual stimuli, the perceived sound may not exist in either modality. This effect has formed the basis for modelling the complementary nature of acoustic and visual speech by encapsulating them into the relatively new research field of audio-visual speech processing (AVSP). Traditional acoustic based speech processing systems have attained a high level of performance in recent years, but the performance of these systems is heavily dependent on a match between training and testing conditions. In the presence of mismatched conditions (eg. acoustic noise) the performance of acoustic speech processing applications can degrade markedly. AVSP aims to increase the robustness and performance of conventional speech processing applications through the integration of the acoustic and visual modalities of speech, in particular the tasks of isolated word speech and text-dependent speaker recognition. Two major problems in AVSP are addressed in this thesis, the first of which concerns the extraction of pertinent visual features for effective speech reading and visual speaker recognition. Appropriate representations of the mouth are explored for improved classification performance for speech and speaker recognition. Secondly, there is the question of how to effectively integrate the acoustic and visual speech modalities for robust and improved performance. This question is explored in-depth using hidden Markov model(HMM)classifiers. The development and investigation of integration strategies for AVSP required research into a new branch of pattern recognition known as classifier combination theory. A novel framework is presented for optimally combining classifiers so their combined performance is greater than any of those classifiers individually. The benefits of this framework are not restricted to AVSP, as they can be applied to any task where there is a need for combining independent classifiers.
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Al-Otaibi, Abdulhadi S. "Arabic speech processing : syllabic segmentation and speech recognition." Thesis, Aston University, 1988. http://publications.aston.ac.uk/8064/.

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A detailed description of the Arabic Phonetic System is given. The syllabic behaviour of the Arabic language is highlighted. Basic statistical properties Of the Arabic language (phoneme and syllabic frequency of repetition) are included. A thorough review of the speech processing techniques, used in speech analysis, synthesis and recognition applications are presented. The development of a PC-based speech processing system is described. The system has proven to be a useful tool in Arabic speech analysis and recognition applications. A sample speotrographic study of two pairs of Arabic similar sounds was performed. it is shown that no clear acoustical property exist in distinguishing between the phonemes /O/ and /f/ except the gradual rise of F1 during formant movements (transitions). The development of an automatic Arabic syllabic segmentation algorithm is described. The performance of the algorithm is tested with monosyllabic and multisyllabic words. An overall accuracy of 92% was achieved. The main parameters affecting the accuracy of the segmentation algorithm are discussed. The syllabic units generated from applying the Arabic syllabic segmentation algorithm are utilized in the implementation of three major speech applications, namely, automatic Arabic vowel recognition system, isolated word recognition system and an acoustic-phonetic model for Arabic. Each application is fully described and its performance results are indicated.
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Grancharov, Volodya. "Human perception in speech processing." Doctoral thesis, Stockholm : Sound and Image Processing Laboratory, School of Electrical Engineering, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4032.

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Duffy, Hester Elizabeth Sarah. "The processing of accented speech." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1556.

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This thesis examines the processing of accented speech in both infants and adults. Accents provide a natural and reasonably consistent form of inter-speaker variation in the speech signal, but it is not yet clear exactly what processes are used to normalise this form of variation, or when and how those processes develop. Two adult studies use ERP data to examine differences between the online processing of regional- and foreign-accented speech as compared to a baseline consisting of the listeners’ home accent. These studies demonstrate that the two types of accents recruit normalisation processes which are qualitatively, and not just quantitatively, different. This provided support for the hypothesis that foreign and regional accents require different mechanisms to normalise accent-based variation (Adank et al., 2009, Floccia et al., 2009), rather than for the hypothesis that different types of accents are normalised according to their perceptual distance from the listener’s own accent (Clarke & Garrett, 2004). They also provide support for the Abstract entry approach to lexical storage of variant forms, which suggests that variant forms undergo a process of prelexical normalisation, allowing access to a canonical lexical entry (Pallier et al., 2001), rather than for the Exemplar-based approach, which suggests that variant word-forms are individually represented in the lexicon (Johnson, 1997). Two further studies examined how infants segment words from continuous speech when presented with accented speakers. The first of these includes a set of behavioural experiments, which highlight some methodological issues in the existing literature and offer some potential explanations for conflicting evidence about the age at which infants are able to segment speech. The second uses ERP data to investigate segmentation within and across accents, and provides neurophysiological evidence that 11-month-olds are able to distinguish newly-segmented words at the auditory level even within a foreign accent, or across accents, but that they are more able to treat new word-forms as word-like in a familiar accent than a foreign accent.
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Egorova, Natalia. "Neurobiology of speech act processing." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648313.

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Wu, Lizhong. "Speech processing with neural networks." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259529.

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Saleh, Gaafar Mustafa Kamil. "Bayesian inference in speech processing." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627179.

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Han, Kun. "Supervised Speech Separation And Processing." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1407865723.

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Books on the topic "Speech processing"

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1945-, Rowden Chris, ed. Speech processing. London: McGraw-Hill, 1992.

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Ince, A. Nejat, ed. Digital Speech Processing. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-2148-5.

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Bailly, Gerard, Pascal Perrier, and Eric Vatikiotis-Bateson, eds. Audiovisual Speech Processing. Cambridge: Cambridge University Press, 2012. http://dx.doi.org/10.1017/cbo9780511843891.

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1964-, Schultz Tanja, and Kirchhoff Katrin, eds. Multilingual speech processing. Amsterdam: Elsevier Academic Press, 2006.

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Frank, Fallside, and Woods William A, eds. Computer speech processing. Englewood Cliffs, (N.J.): Prentice-Hall, 1985.

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

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Audiovisual speech processing. Cambridge: Cambridge University Press, 2012.

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Buydos, John F. Speech recognition and processing. Washington, D.C: Science Reference Section, Science and Technology Division, Library of Congress, 1994.

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Wang, Jhing-Fa, Sadaoki Furui, and Biing-Hwang Juang, eds. Real World Speech Processing. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-6363-8.

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

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Book chapters on the topic "Speech processing"

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Frerking, Marvin E. "Speech Processing." In Digital Signal Processing in Communication Systems, 490–547. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-4990-8_9.

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Blanchet, Gérard, and Maurice Charbit. "Speech Processing." In Digital Signal and Image Processing Using Matlab®, 105–30. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118999592.ch5.

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Ince, A. Nejat. "Speech Processing Standards." In The Kluwer International Series in Engineering and Computer Science, 161–88. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-2148-5_7.

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Pathak, Manas A. "Speech Processing Background." In Privacy-Preserving Machine Learning for Speech Processing, 7–18. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4639-2_2.

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Moore, Roger K. "Speech Pattern Processing." In Computational Models of Speech Pattern Processing, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_1.

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Apte, Shaila Dinkar. "Statistical Speech Processing." In Random Signal Processing, 285–311. Boca Raton : CRC Press, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155357-7.

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Chandrasekaran, Bharath, Rachel Tessmer, and G. Nike Gnanateja. "Subcortical Processing of Speech Sounds." In Speech Perception, 13–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81542-4_2.

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Owens, F. J. "Digital Speech." In Signal Processing of Speech, 17–34. London: Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-22599-6_2.

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Owens, F. J. "Speech Synthesis." In Signal Processing of Speech, 88–121. London: Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-22599-6_5.

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Owens, F. J. "Speech Coding." In Signal Processing of Speech, 122–37. London: Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-22599-6_6.

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Conference papers on the topic "Speech processing"

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"Signal Processing. Speech and Audio Processing." In 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854416.

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Ali, Syed Imran, Raza Hasan, and M. Sohail Hayat. "Speech and audio processing laboratory: Speech coding related signal processing modules." In 2015 2nd World Symposium on Web Applications and Networking (WSWAN). IEEE, 2015. http://dx.doi.org/10.1109/wswan.2015.7210356.

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Glass, James. "Towards unsupervised speech processing." In 2012 11th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2012. http://dx.doi.org/10.1109/isspa.2012.6310546.

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Ressl, Marc, Jorge Prendes, and Roxana Saint-Nom. "Undergraduate speech processing awareness." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288492.

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"Session WA2b: Speech processing." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335704.

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Massaro, Dominic W. "Auditory visual speech processing." In 7th European Conference on Speech Communication and Technology (Eurospeech 2001). ISCA: ISCA, 2001. http://dx.doi.org/10.21437/eurospeech.2001-299.

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"Session WA8b1 Speech Processing." In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. IEEE, 2004. http://dx.doi.org/10.1109/acssc.2004.1399566.

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Huckvale, Mark. "Speech synthesis, speech simulation and speech science." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA: ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-388.

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Anu, J. P., and Veena Karjigi. "Sentence segmentation for speech processing." In 2014 National Conference on Communication, Signal Processing and Networking (NCCSN). IEEE, 2014. http://dx.doi.org/10.1109/nccsn.2014.7001148.

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Arshad, N. W., S. N. Abdul Aziz, R. Hamid, R. Abdul Karim, F. Naim, and N. F. Zakaria. "Speech processing for makhraj recognition." In 2011 International Conference on Electrical, Control and Computer Engineering (INECCE). IEEE, 2011. http://dx.doi.org/10.1109/inecce.2011.5953900.

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Reports on the topic "Speech processing"

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Sabrin, Howard. UNIX Speech Processing Development. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada332983.

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Samuel, Arthur G. Levels of Processing of Speech and Non-Speech. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada237796.

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Hoeferlin, David M., Brian M. Ore, Stephen A. Thorn, and David Snyder. Speech Processing and Recognition (SPaRe). Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada540142.

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Varchavskaia, Paulina, Paul Fitzpatrick, and Cynthia Breazeal. Characterizing and Processing Robot-Directed Speech. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada457057.

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Liu, Fu-Hua, Pedro J. Moreno, Richard M. Stern, and Alejandro Acero. Signal Processing for Robust Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada457798.

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Ghitza, Oded. Auditory Peripheral Processing of Degraded Speech. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada420098.

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Gordon, Peter C. Multi-Level Processing in Human Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada216475.

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Hansen, John H. Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment. Fort Belvoir, VA: Defense Technical Information Center, October 2015. http://dx.doi.org/10.21236/ada623029.

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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), September 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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Koffley, Lynn M. Real-Time Super-High-Speed-Processing. Fort Belvoir, VA: Defense Technical Information Center, March 1996. http://dx.doi.org/10.21236/ada305394.

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