Relevant bibliographies by topics / Speech

Academic literature on the topic 'Speech'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Speech"

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Mohamad Nasir, A. B., N. R. M. Nasir, and F. H. M. Salleh. "SPEESH: speech-based mobile application for dysarthric speech recognition." Journal of Physics: Conference Series 1860, no. 1 (March 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/1860/1/012003.

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Rogers, David, and Geoffrey Hill. "Speech! Speech!" World Literature Today 76, no. 1 (2002): 152. http://dx.doi.org/10.2307/40157092.

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Mohammed Hashim, Suhair Safwat. "Speech Acts in Political Speeches." Journal of Modern Education Review 5, no. 7 (July 20, 2015): 699–706. http://dx.doi.org/10.15341/jmer(2155-7993)/07.05.2015/008.

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Laird, Andrew. "Speech in Speech." Classical Review 49, no. 2 (October 1999): 417–18. http://dx.doi.org/10.1093/cr/49.2.417.

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Furui, S., T. Kikuchi, Y. Shinnaka, and C. Hori. "Speech-to-Text and Speech-to-Speech Summarization of Spontaneous Speech." IEEE Transactions on Speech and Audio Processing 12, no. 4 (July 2004): 401–8. http://dx.doi.org/10.1109/tsa.2004.828699.

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Park, Seongjin. "Interpretation of speech rhythm: Speech error, speech rhythm, and speech proficiency." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A343. http://dx.doi.org/10.1121/10.0019094.

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It has been well-known that second language learners are affected by their first language when producing their L2. For speech rhythm, it has been suggested that L2 speakers are affected by L1 speech rhythm (e.g., Korean learners of English produce English without reducing the duration of unstressed vowels), and the effect is greater when speakers are beginner or intermediate-level language learners. This study, however, suggests that the direction of the effect is not always the same as researchers expected, and shows how easily speech rhythm is influenced by speech errors. The result of this study shows the relationship between the type of speech errors and speech rhythm metrics, and how that affects the perceptual proficiency of L2 speakers as well as L1 speakers. Future studies will be conducted to examine the way to infer the type of speech errors using speech rhythm metrics.
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Janai, Siddhanna, Shreekanth T., Chandan M., and Ajish K. Abraham. "Speech-to-Speech Conversion." International Journal of Ambient Computing and Intelligence 12, no. 1 (January 2021): 184–206. http://dx.doi.org/10.4018/ijaci.2021010108.

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A novel approach to build a speech-to-speech conversion (STSC) system for individuals with speech impairment dysarthria is described. STSC system takes impaired speech having inherent disturbance as input and produces a synthesized output speech with good pronunciation and noise free utterance. The STSC system involves two stages, namely automatic speech recognition (ASR) and automatic speech synthesis. ASR transforms speech into text, while automatic speech synthesis (or text-to-speech [TTS]) performs the reverse task. At present, the recognition system is developed for a small vocabulary of 50 words and the accuracy of 94% is achieved for normal speakers and 88% for speakers with dysarthria. The output speech of TTS system has achieved a MOS value of 4.5 out of 5 as obtained by averaging the response of 20 listeners. This method of STSC would be an augmentative and alternative communication aid for speakers with dysarthria.
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Widari, Kadek, and Ni Luh Yaniasti. "Japanese Directive Speech." International Journal of Multidisciplinary Sciences 1, no. 2 (June 1, 2023): 147–58. http://dx.doi.org/10.37329/ijms.v1i2.2285.

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Language in role as a means of communication could result in relationships between speakers of to said. The use of language in the process of communication is very important, because community life is made possible by speech. Speechs are used to information of ideas, intentions directly or indirectly. Speech act serves to declare mean it is speakers by speech partners of said. Directive speech is the type of a speech that used by speakers of to send the speech partner said do something. The use of directive speech in conveying a speech should look factors affecting the speech. The porpuse in this research was to identify the directive speech acts and factors that affect the level of politeness. The method in this research used qualitative method with descriptive analysis method. The theory in this research used were speech act theory by Yule and politeness theory by Mizutani. The results of the analysis, the forms of directive speech acts contained in the comic Ore wo Suki Nano wa Omae dake kayo are 1) directive speech acts of command marked by tamae and nasai, 2) directive speech acts of requests marked by te kure, naide kure, te kudasai, tte, te, and te hoshii; 3) the directive speech act of an invitation is marked with mashou; 4) directive speech acts of permission marked by te mo ii and 5) directive speech acts of suggestions marked with houga ii. Directive speech is influenced by several factors, namely: 1) familiarity factor; 2) age; 3) social relationship; 4) gender and 5) situation.
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Oder, Alp Bugra. "Speech Acts Revisited: Examining Illocutionary Speech Acts in Speeches of Mustafa Kemal Ataturk." Proceedings of The International Conference on Research in Humanities and Social Sciences 1, no. 1 (December 22, 2023): 24–35. http://dx.doi.org/10.33422/icrhs.v1i1.130.

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Pragmatics is an interdisciplinary subfield of applied linguistics that investigates the meaning in context. One of its research areas, speech acts, provides important implications on how the meaning behind the utterances is perceived and what effect it may have on the hearer. Theories and classifications proposed by Austin (1962) and Searle (1979) are particularly useful in understanding the hidden meaning and its effect on the audience. Political discourse is directly connected with speech acts and there is a body of research that focuses on the classification of illocutionary acts embedded within speeches of politicians. In this regard, the present research aimed to analyze illocutionary speech acts of two speeches of Mustafa Kemal Ataturk;speech at the 10th anniversary of Turkish Republic and Address to Turkish Youth which was a part of his great speech that he delivered to deputies and representatives of the Republican Party on 15th-20th October 1927 by employing qualitative content analysis on English translations of the speeches. Subsequent to meticulous analysis, the present qualitative study concluded that Ataturk used more speech acts in his speech at the 10th anniversary of the Turkish Republic than his Address to Turkish Youth. Speech acts in his speech at the 10th anniversary of the Turkish Republic primarily featured expressive, representative, commissive and directive speech acts while his Address to Turkish Youth featured representative and commissive, directive, expressive speech acts, respectively.In total, the most used speech acts were representatives, followed by expressives, commissives and directives. No declaration speech act was observed in either speech.
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Chen, Jing, Xihong H. Wu, Xuefei F. Zou, Zhiping P. Zhang, Lijuan J. Xu, Mengyuan Y. Wang, Liang Li, and Huisheng S. Chi. "Effect of speech rate on speech‐on‐speech masking." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3713. http://dx.doi.org/10.1121/1.2935152.

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Dissertations / Theses on the topic "Speech"

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Sun, Felix (Felix W. ). "Speech Representation Models for Speech Synthesis and Multimodal Speech Recognition." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106378.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-63).
The field of speech recognition has seen steady advances over the last two decades, leading to the accurate, real-time recognition systems available on mobile phones today. In this thesis, I apply speech modeling techniques developed for recognition to two other speech problems: speech synthesis and multimodal speech recognition with images. In both problems, there is a need to learn a relationship between speech sounds and another source of information. For speech synthesis, I show that using a neural network acoustic model results in a synthesizer that is more tolerant of noisy training data than previous work. For multimodal recognition, I show how information from images can be effectively integrated into the recognition search framework, resulting in improved accuracy when image data is available.
by Felix Sun.
M. Eng.
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Alcaraz, Meseguer Noelia. "Speech Analysis for Automatic Speech Recognition." Thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9092.

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The classical front end analysis in speech recognition is a spectral analysis which parametrizes the speech signal into feature vectors; the most popular set of them is the Mel Frequency Cepstral Coefficients (MFCC). They are based on a standard power spectrum estimate which is first subjected to a log-based transform of the frequency axis (mel- frequency scale), and then decorrelated by using a modified discrete cosine transform. Following a focused introduction on speech production, perception and analysis, this paper gives a study of the implementation of a speech generative model; whereby the speech is synthesized and recovered back from its MFCC representations. The work has been developed into two steps: first, the computation of the MFCC vectors from the source speech files by using HTK Software; and second, the implementation of the generative model in itself, which, actually, represents the conversion chain from HTK-generated MFCC vectors to speech reconstruction. In order to know the goodness of the speech coding into feature vectors and to evaluate the generative model, the spectral distance between the original speech signal and the one produced from the MFCC vectors has been computed. For that, spectral models based on Linear Prediction Coding (LPC) analysis have been used. During the implementation of the generative model some results have been obtained in terms of the reconstruction of the spectral representation and the quality of the synthesized speech.

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Kleinschmidt, Tristan Friedrich. "Robust speech recognition using speech enhancement." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/31895/1/Tristan_Kleinschmidt_Thesis.pdf.

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Automatic Speech Recognition (ASR) has matured into a technology which is becoming more common in our everyday lives, and is emerging as a necessity to minimise driver distraction when operating in-car systems such as navigation and infotainment. In “noise-free” environments, word recognition performance of these systems has been shown to approach 100%, however this performance degrades rapidly as the level of background noise is increased. Speech enhancement is a popular method for making ASR systems more ro- bust. Single-channel spectral subtraction was originally designed to improve hu- man speech intelligibility and many attempts have been made to optimise this algorithm in terms of signal-based metrics such as maximised Signal-to-Noise Ratio (SNR) or minimised speech distortion. Such metrics are used to assess en- hancement performance for intelligibility not speech recognition, therefore mak- ing them sub-optimal ASR applications. This research investigates two methods for closely coupling subtractive-type enhancement algorithms with ASR: (a) a computationally-efficient Mel-filterbank noise subtraction technique based on likelihood-maximisation (LIMA), and (b) in- troducing phase spectrum information to enable spectral subtraction in the com- plex frequency domain. Likelihood-maximisation uses gradient-descent to optimise parameters of the enhancement algorithm to best fit the acoustic speech model given a word se- quence known a priori. Whilst this technique is shown to improve the ASR word accuracy performance, it is also identified to be particularly sensitive to non-noise mismatches between the training and testing data. Phase information has long been ignored in spectral subtraction as it is deemed to have little effect on human intelligibility. In this work it is shown that phase information is important in obtaining highly accurate estimates of clean speech magnitudes which are typically used in ASR feature extraction. Phase Estimation via Delay Projection is proposed based on the stationarity of sinusoidal signals, and demonstrates the potential to produce improvements in ASR word accuracy in a wide range of SNR. Throughout the dissertation, consideration is given to practical implemen- tation in vehicular environments which resulted in two novel contributions – a LIMA framework which takes advantage of the grounding procedure common to speech dialogue systems, and a resource-saving formulation of frequency-domain spectral subtraction for realisation in field-programmable gate array hardware. The techniques proposed in this dissertation were evaluated using the Aus- tralian English In-Car Speech Corpus which was collected as part of this work. This database is the first of its kind within Australia and captures real in-car speech of 50 native Australian speakers in seven driving conditions common to Australian environments.
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Blank, Sarah Catrin. "Speech comprehension, speech production and recovery of propositional speech following aphasic stroke." Thesis, Imperial College London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407772.

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Price, Moneca C. "Interactions between speech coders and disordered speech." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ28640.pdf.

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Chong, Fong Loong. "Objective speech quality measurement for Chinese speech." Thesis, University of Canterbury. Computer Science and Software Engineering, 2005. http://hdl.handle.net/10092/9607.

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In the search for the optimisation of transmission speed and storage, speech information is often coded, or transmitted with a reduced bandwidth. As a result, quality and/or intelligibility are sometimes degraded. Speech quality is normally defined as the degree of goodness in the perception of speech while speech intelligibility is how well or clearly one can understand what is being said. In order to assess the level of acceptability of degraded speeches, various subjective methods have been developed to test codecs or sound processing systems. Although good results have been demonstrated with these, they are time consuming and expensive due to the necessary involvement of teams of professional or naive subjects1[56]. To reduce cost, computerised objective systems were created with the hope of replacing human subjects [90][43]. While reasonable standards have been reported by several of these systems, they have not reached the accuracy of well constructed subjective tests yet [92][84]. Therefore, their evaluations and improvements are constantly been researched for further breakthroughs. To date, objective speech quality measurement systems (OSQMs) have been developed mostly in Europe or the United States, and effectiveness is only tested for English, several European and Asian languages but not Chinese (Mandarin) [38][70][32].
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Stedmon, Alexander Winstan. "Putting speech in, taking speech out : human factors in the use of speech interfaces." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420342.

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Miyajima, C., D. Negi, Y. Ninomiya, M. Sano, K. Mori, K. Itou, K. Takeda, and Y. Suenaga. "Audio-Visual Speech Database for Bimodal Speech Recognition." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10460.

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Tang, Lihong. "Nonsensical speech : speech acts in postsocialist Chinese culture /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/6662.

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Itakura, Fumitada, Tetsuya Shinde, Kiyoshi Tatara, Taisuke Ito, Ikuya Yokoo, Shigeki Matsubara, Kazuya Takeda, and Nobuo Kawaguchi. "CIAIR speech corpus for real world speech recognition." The oriental chapter of COCOSDA (The International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques), 2002. http://hdl.handle.net/2237/15462.

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Books on the topic "Speech"

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Hill, Geoffrey. Speech! Speech! London: Penguin Books, 2001.

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Kidawara, Yutaka, Eiichiro Sumita, and Hisashi Kawai, eds. Speech-to-Speech Translation. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0595-9.

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Kitano, Hiroaki. Speech-to-Speech Translation. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2732-9.

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Ayers, Joe. Speech sampler: Speeches and analyses. 2nd ed. Ruston, Wash: Communication Ventures, 1999.

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Affairs, Canada Dept of External. Speech. S.l: s.n, 1988.

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Checkland, Michael. Speech. London: BBC Corporate Press Office, 1987.

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Hardcastle, William J. Speech Production and Speech Modelling. Dordrecht: Springer Netherlands, 1990.

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NATO, Advanced Study Institute on Speech Production and Speech Modelling (1st 1989 Bonas France). Speech production and speech modelling. Dordrecht, Netherlands: Kluwer Academic Publishers, 1990.

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Spicer, Robert N. Free Speech and False Speech. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69820-5.

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Hardcastle, William J., and Alain Marchal, eds. Speech Production and Speech Modelling. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-2037-8.

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Book chapters on the topic "Speech"

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Danon-Boileau, Laurent. "Associative Speech, Compulsive Speech." In Psychoanalysts in Session, 7–11. Abingdon, Oxon; New York, NY: Routledge, 2021. | Series: The new library of psychoanalysis | “Published in French, 2016”–Title page verso.: Routledge, 2020. http://dx.doi.org/10.4324/9780429196751-1a.

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Hartmann, William M. "Speech." In Principles of Musical Acoustics, 227–36. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6786-1_22.

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Turkstra, Lyn S. "Speech." In Encyclopedia of Clinical Neuropsychology, 3243. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_923.

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Turkstra, Lyn S. "Speech." In Encyclopedia of Clinical Neuropsychology, 1. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_923-3.

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Weik, Martin H. "speech." In Computer Science and Communications Dictionary, 1635. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17909.

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Bean Ellawadi, Allison. "Speech." In Encyclopedia of Autism Spectrum Disorders, 1. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-6435-8_1699-3.

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Bean, Allison. "Speech." In Encyclopedia of Autism Spectrum Disorders, 2953. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_1699.

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Howard-Jones, Paul. "Speech." In Evolution of the Learning Brain, 101–18. Abingdon, Oxon ; New York, NY : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315150857-6.

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Lucey, Michael. "Speech." In The Proustian Mind, 191–207. London: Routledge, 2022. http://dx.doi.org/10.4324/9780429341472-16.

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Tait, James. "Speech." In Entering Architectural Practice, 317–31. Abingdon, Oxon ; New York : Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9780429346569-22.

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Conference papers on the topic "Speech"

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Alonso-Mora, Javier. "Keynote Speeches *Ranked by speech time Keynote Speech 1." In 2020 3rd International Conference on Unmanned Systems (ICUS). IEEE, 2020. http://dx.doi.org/10.1109/icus50048.2020.9274922.

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Newell, Alan F., John L. Arnott, and R. Dye. "A full speed speech simulation of speech recognition machines." In European Conference on Speech Technology. ISCA: ISCA, 1987. http://dx.doi.org/10.21437/ecst.1987-207.

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Louw, Johannes A., Daniel R. van Niekerk, and Georg I. Schlünz. "Introducing the Speect speech synthesis platform." In The Blizzard Challenge 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/blizzard.2010-4.

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Zhu, Jinwei, Huan Chen, Xing Wen, Zhenlin Huang, and Liuqi Zhao. "An Adaptive Speech Speed Algorithm for Improving Continuous Speech Recognition." In ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3650215.3650322.

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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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Mineeva, M. I., and D. A. Korneev. "Low-Speed Speech Compression Systems." In 2023 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2023. http://dx.doi.org/10.1109/dynamics60586.2023.10349495.

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Lin, Weigan. "Cassinian Waveguides [Keynote Speeches: Speech 1]." In 2007 5th International Conference on Communications, Circuits and Systems. IEEE, 2007. http://dx.doi.org/10.1109/icccas.2007.4348120.

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Izzad, M., Nursuriati Jamil, and Zainab Abu Bakar. "Speech/non-speech detection in Malay language spontaneous speech." In 2013 International Conference on Computing, Management and Telecommunications (ComManTel). IEEE, 2013. http://dx.doi.org/10.1109/commantel.2013.6482394.

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Krishna, Gautam, Co Tran, Jianguo Yu, and Ahmed H. Tewfik. "Speech Recognition with No Speech or with Noisy Speech." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683453.

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Clark, Leigh, Benjamin R. Cowan, Abi Roper, Stephen Lindsay, and Owen Sheers. "Speech diversity and speech interfaces." In CUI '20: 2nd Conference on Conversational User Interfaces. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3405755.3406139.

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Reports on the topic "Speech"

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Weinstein, C. J. Speech-to-Speech Translation: Technology and Applications Study. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada401684.

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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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Gordon, Jane. Use of synthetic speech in tests of speech discrimination. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5327.

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Ore, Brian M. Speech Recognition, Articulatory Feature Detection, and Speech Synthesis in Multiple Languages. Fort Belvoir, VA: Defense Technical Information Center, November 2009. http://dx.doi.org/10.21236/ada519140.

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Artayev, S. N., L. A. Lidzhiyeva, and ZH A. Mukabenova. Listen to native speech! OFERNIO, February 2019. http://dx.doi.org/10.12731/ofernio.2019.24050.

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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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Hogden, J. An articulatorily constrained, maximum entropy approach to speech recognition and speech coding. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/432946.

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Shao, Yang, Soundararajan Srinivasan, Zhaozhang Jin, and DeLiang Wang. A Computational Auditory Scene Analysis System for Speech Segregation and Robust Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ad1001212.

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Josephson, John, and James Russell. Separation of Speech from Background. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada428696.

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Maybury, Mark T. Auditory Models for Speech Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada197322.

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