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Relevant bibliographies by topics / Automatic speech recognition – Statistical methods

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Academic literature on the topic 'Automatic speech recognition – Statistical methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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Journal articles on the topic "Automatic speech recognition – Statistical methods"

1

Boyer, A., J. Di Martino, P. Divoux, J. P. Haton, J. F. Mari, and K. Smaili. "Statistical methods in multi-speaker automatic speech recognition." Applied Stochastic Models and Data Analysis 6, no. 3 (September 1990): 143–55. http://dx.doi.org/10.1002/asm.3150060302.

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Kłosowski, Piotr. "A Rule-Based Grapheme-to-Phoneme Conversion System." Applied Sciences 12, no. 5 (March 7, 2022): 2758. http://dx.doi.org/10.3390/app12052758.

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This article presents a rule-based grapheme-to-phoneme conversion method and algorithm for Polish. It should be noted that the fundamental grapheme-to-phoneme conversion rules have been developed by Maria Steffen-Batóg and presented in her set of monographs dedicated to the automatic grapheme-to-phoneme conversion of texts in Polish. The author used previously developed rules and independently developed the grapheme-to-phoneme conversion algorithm.The algorithm has been implemented as a software application called TransFon, which allows the user to convert any text in Polish orthography to cor

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Toth, Laszlo, Ildiko Hoffmann, Gabor Gosztolya, Veronika Vincze, Greta Szatloczki, Zoltan Banreti, Magdolna Pakaski, and Janos Kalman. "A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech." Current Alzheimer Research 15, no. 2 (January 3, 2018): 130–38. http://dx.doi.org/10.2174/1567205014666171121114930.

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Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening softwa

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Gellatly, Andrew W., and Thomas A. Dingus. "Speech Recognition and Automotive Applications: Using Speech to Perform in-Vehicle Tasks." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 17 (October 1998): 1247–51. http://dx.doi.org/10.1177/154193129804201715.

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An experiment was conducted to investigate the effects of automatic speech recognition (ASR) system design, driver input-modality, and driver age on driving performance during in-vehicle task execution and in-vehicle task usability. Results showed that ASR system design (i.e., recognition accuracy and recognition error type) and driver input-modality (i.e., manual or speech) significantly affected certain dependent measures. However, the differences found were small, suggesting that less than ideal ASR system design/performance can be considered for use in automobiles without substantially imp

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Seman, Noraini, and Ahmad Firdaus Norazam. "Hybrid methods of brandt’s generalised likelihood ratio and short-term energy for malay word speech segmentation." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 283. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp283-291.

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<p>Speech segmentation is an important part for speech recognition, synthesizing and coding. Statistical based approach detects segmentation points via computing spectral distortion of the signal without prior knowledge of the acoustic information proved to be able to give good match, less omission but lot of insertion. In this study the segmentation is done both manually and automatically using Malay words in traditional Malay poetry. This study proposed a hybrid method of Brandt’s generalized likelihood ratio (GLR) and short-term energy algorithm. The Brandt’s algorithm tries to estima

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Cabral, Frederico Soares, Hidekazu Fukai, and Satoshi Tamura. "Feature Extraction Methods Proposed for Speech Recognition Are Effective on Road Condition Monitoring Using Smartphone Inertial Sensors." Sensors 19, no. 16 (August 9, 2019): 3481. http://dx.doi.org/10.3390/s19163481.

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The objective of our project is to develop an automatic survey system for road condition monitoring using smartphone devices. One of the main tasks of our project is the classification of paved and unpaved roads. Assuming recordings will be archived by using various types of vehicle suspension system and speeds in practice, hence, we use the multiple sensors found in smartphones and state-of-the-art machine learning techniques for signal processing. Despite usually not being paid much attention, the results of the classification are dependent on the feature extraction step. Therefore, we have

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Hai, Yanfei. "Computer-aided teaching mode of oral English intelligent learning based on speech recognition and network assistance." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5749–60. http://dx.doi.org/10.3233/jifs-189052.

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The purpose of this paper is to use English specific syllables and prosodic features in spoken speech data to carry out English spoken recognition, and to explore effective methods for the design and application of English speech detection and automatic recognition systems. The method proposed by this study is a combination of SVM_FF based classifier, SVM_IER based classifier and syllable classifier. Compared with the method based on the combination of other phonological characteristics such as phonological rate, intensity, formant and energy statistics and pronunciation rate, and the syllable

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Markovnikov, Nikita, and Irina Kipyatkova. "Encoder-decoder models for recognition of Russian speech." Information and Control Systems, no. 4 (October 4, 2019): 45–53. http://dx.doi.org/10.31799/1684-8853-2019-4-45-53.

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Problem: Classical systems of automatic speech recognition are traditionally built using an acoustic model based on hidden Markovmodels and a statistical language model. Such systems demonstrate high recognition accuracy, but consist of several independentcomplex parts, which can cause problems when building models. Recently, an end-to-end recognition method has been spread, usingdeep artificial neural networks. This approach makes it easy to implement models using just one neural network. End-to-end modelsoften demonstrate better performance in terms of speed and accuracy of speech recognitio

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AFLI, HAITHEM, LOÏC BARRAULT, and HOLGER SCHWENK. "Building and using multimodal comparable corpora for machine translation." Natural Language Engineering 22, no. 4 (June 15, 2016): 603–25. http://dx.doi.org/10.1017/s1351324916000152.

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AbstractIn recent decades, statistical approaches have significantly advanced the development of machine translation systems. However, the applicability of these methods directly depends on the availability of very large quantities of parallel data. Recent works have demonstrated that a comparable corpus can compensate for the shortage of parallel corpora. In this paper, we propose an alternative to comparable corpora containing text documents as resources for extracting parallel data: a multimodal comparable corpus with audio documents in source language and text document in target language,

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Kozlova, A. T. "Temporal Characteristics of Prosody in Imperative Utterances and the Phenomenon of Emphatic Length in the English Language." Bulletin of Kemerovo State University, no. 3 (October 27, 2018): 192–96. http://dx.doi.org/10.21603/2078-8975-2018-3-192-196.

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The paper focuses on one of the most effective factors of linguistic manipulation, i.e. imperative utterance. The subject of the study was direct contact appeals, whose structures corresponded to the literary norms of the English language. The research determined and described the temporal component of imperative prosody. The author employed electro-acoustic, mathematical and statistical methods. The phonetic experiment revealed four prosodic structures, as well as their inter-structural and inter-style levels, the degree of temporal fluctuation and the phenomenon of emphatic length, the latte

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Dissertations / Theses on the topic "Automatic speech recognition – Statistical methods"

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Wu, Jian, and 武健. "Discriminative speaker adaptation and environmental robustness in automatic speech recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31246138.

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黃伯光 and Pak-kwong Wong. "Statistical language models for Chinese recognition: speech and character." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31239456.

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Chan, Oscar. "Prosodic features for a maximum entropy language model." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0244.

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A statistical language model attempts to characterise the patterns present in a natural language as a probability distribution defined over word sequences. Typically, they are trained using word co-occurrence statistics from a large sample of text. In some language modelling applications, such as automatic speech recognition (ASR), the availability of acoustic data provides an additional source of knowledge. This contains, amongst other things, the melodic and rhythmic aspects of speech referred to as prosody. Although prosody has been found to be an important factor in human speech recognitio

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Fu, Qiang. "A generalization of the minimum classification error (MCE) training method for speech recognition and detection." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22705.

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The model training algorithm is a critical component in the statistical pattern recognition approaches which are based on the Bayes decision theory. Conventional applications of the Bayes decision theory usually assume uniform error cost and result in a ubiquitous use of the maximum a posteriori (MAP) decision policy and the paradigm of distribution estimation as practice in the design of a statistical pattern recognition system. The minimum classification error (MCE) training method is proposed to overcome some substantial limitations for the conventional distribution estimation methods. In t

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Seward, Alexander. "Efficient Methods for Automatic Speech Recognition." Doctoral thesis, KTH, Tal, musik och hörsel, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3675.

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This thesis presents work in the area of automatic speech recognition (ASR). The thesis focuses on methods for increasing the efficiency of speech recognition systems and on techniques for efficient representation of different types of knowledge in the decoding process. In this work, several decoding algorithms and recognition systems have been developed, aimed at various recognition tasks. The thesis presents the KTH large vocabulary speech recognition system. The system was developed for online (live) recognition with large vocabularies and complex language models. The system utilizes weight

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Clarkson, P. R. "Adaptation of statistical language models for automatic speech recognition." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597745.

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Statistical language models encode linguistic information in such a way as to be useful to systems which process human language. Such systems include those for optical character recognition and machine translation. Currently, however, the most common application of language modelling is in automatic speech recognition, and it is this that forms the focus of this thesis. Most current speech recognition systems are dedicated to one specific task (for example, the recognition of broadcast news), and thus use a language model which has been trained on text which is appropriate to that task. If, ho

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Wei, Yi. "Statistical methods on automatic aircraft recognition in aerial images." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248947.

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Wong, Pak-kwong. "Statistical language models for Chinese recognition : speech and character /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20158725.

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McGreevy, Michael. "Statistical language modelling for large vocabulary speech recognition." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16444/1/Michael_McGreevy_Thesis.pdf.

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The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands on language models. In a large vocabulary system, acoustic confusion is greater, thus there is more reliance placed on the language model for disambiguation. In addition to this, ASR systems are increasingly being deployed in situations where the speaker is not conscious of their interaction with the system, such as in recorded meetings and surveillance scenarios. This results in more natural speech, which contains many false starts and disfluencies. In this thesis we investigate a novel ap

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McGreevy, Michael. "Statistical language modelling for large vocabulary speech recognition." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16444/.

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The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands on language models. In a large vocabulary system, acoustic confusion is greater, thus there is more reliance placed on the language model for disambiguation. In addition to this, ASR systems are increasingly being deployed in situations where the speaker is not conscious of their interaction with the system, such as in recorded meetings and surveillance scenarios. This results in more natural speech, which contains many false starts and disfluencies. In this thesis we investigate a novel approa

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Books on the topic "Automatic speech recognition – Statistical methods"

1

Statistical methods for speech recognition. Cambridge, Mass: MIT Press, 1997.

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Alumäe, Tanel. Methods for Estonian large vocabulary speech recognition. [Tallinn]: Tallinn University of Technology Press, 2006.

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Joseph, Keshet, and Bengio Samy, eds. Automatic speech and speaker recognition: Large margin and kernel methods. Hoboken, NJ: J. Wiley & Sons, 2009.

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4

Minker, Wolfgang. Incorporating Knowledge Sources into Statistical Speech Recognition. Boston, MA: Springer Science+Business Media, LLC, 2009.

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Jelinek, Frederick. Statistical Methods for Speech Recognition. MIT Press, 2022.

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Keshet, Joseph, and Samy Bengio. Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods. Wiley & Sons, Limited, John, 2009.

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Keshet, Joseph, and Samy Bengio. Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods. Wiley & Sons, Incorporated, John, 2009.

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A, Fourcin, ed. Speech input and output assessment: Multilingual methods and standards. Chichester, West Sussex, England: E. Horwood, 1989.

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Müller, Christian. Speaker Classification I: Fundamentals, Features, and Methods. Springer London, Limited, 2007.

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Lamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Speech recognition is concerned with converting the speech waveform, an acoustic signal, into a sequence of words. Today's approaches are based on a statistical modellization of the speech signal. This article provides an overview of the main topics addressed in speech recognition, which are, acoustic-phonetic modelling, lexical representation, language modelling, decoding, and model adaptation. Language models are used in speech recognition to estimate the probability of word sequences. The main components of a generic speech recognition system are, main knowledge sources, feature analysis, a

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Book chapters on the topic "Automatic speech recognition – Statistical methods"

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de Mori, Renato. "Statistical Methods for Automatic Speech Recognition." In Speech Processing, Recognition and Artificial Neural Networks, 165–89. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0845-0_7.

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Juang, B. H., Wu Chou, and C. H. Lee. "Statistical and Discriminative Methods for Speech Recognition." In Speech Recognition and Coding, 41–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57745-1_4.

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Pironkov, Gueorgui, Sean U. N. Wood, Stéphane Dupont, and Thierry Dutoit. "Investigating a Hybrid Learning Approach for Robust Automatic Speech Recognition." In Statistical Language and Speech Processing, 67–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00810-9_7.

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Mari, Jean-François, and René Schott. "Some Applications in Speech Recognition." In Probabilistic and Statistical Methods in Computer Science, 153–76. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-6280-8_4.

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Fuhrmann, Ferdinand, Anna Maly, Christina Leitner, and Franz Graf. "Three Experiments on the Application of Automatic Speech Recognition in Industrial Environments." In Statistical Language and Speech Processing, 109–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68456-7_9.

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Juang, B. H., W. Chou, and C. H. Lee. "Statistical and Discriminative Methods for Speech Recognition." In The Kluwer International Series in Engineering and Computer Science, 109–32. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1367-0_5.

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Hodžić, Migdat, and Tarik Namas. "Automatic SAR Target Recognition and Pose Estimation. Part 2. Statistical Methods for Target Recognition." In Lecture Notes in Networks and Systems, 901–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71321-2_77.

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Coleti, Thiago Adriano, Marcelo Morandini, and Fátima de Lourdes dos Santos Nunes. "Analyzing Face and Speech Recognition to Create Automatic Information for Usability Evaluation." In Human-Computer Interaction. Human-Centred Design Approaches, Methods, Tools, and Environments, 184–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39232-0_21.

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Korenevsky, M. L., Yu N. Matveev, and A. V. Yakovlev. "Investigation and Development of Methods for Improving Robustness of Automatic Speech Recognition Algorithms in Complex Acoustic Environments." In Proceedings of the Scientific-Practical Conference "Research and Development - 2016", 11–20. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62870-7_2.

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Emiliani, U., P. Podini, and F. Sani. "Combined Application of Neural Network and Artificial Intelligence Methods to Automatic Speech Recognition in a Continuous Utterance." In Artificial Neural Nets and Genetic Algorithms, 269–74. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0_40.

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Conference papers on the topic "Automatic speech recognition – Statistical methods"

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Drygajlo, Andrzej, Didier Meuwly, and Anil Alexander. "Statistical methods and Bayesian interpretation of evidence in forensic automatic speaker recognition." In 8th European Conference on Speech Communication and Technology (Eurospeech 2003). ISCA: ISCA, 2003. http://dx.doi.org/10.21437/eurospeech.2003-297.

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Xiao, Xiaoqiang, Jasha Droppo, and Alex Acero. "Information retrieval methods for automatic speech recognition." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495229.

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Kozierski, Piotr, Talar Sadalla, Szymon Drgas, and Adam Dabrowski. "Allophones in automatic whispery speech recognition." In 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2016. http://dx.doi.org/10.1109/mmar.2016.7575241.

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Furui, Sadaoki. "Robust methods in automatic speech recognition and understanding." In 8th European Conference on Speech Communication and Technology (Eurospeech 2003). ISCA: ISCA, 2003. http://dx.doi.org/10.21437/eurospeech.2003-575.

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Lehning, Michael. "Statistical methods for the automatic labelling of German prosody." In 4th European Conference on Speech Communication and Technology (Eurospeech 1995). ISCA: ISCA, 1995. http://dx.doi.org/10.21437/eurospeech.1995-500.

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Prodeus, Arkadiy, and Kateryna Kukharicheva. "Training of automatic speech recognition system on noised speech." In 2016 4th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC). IEEE, 2016. http://dx.doi.org/10.1109/msnmc.2016.7783147.

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Schatzmann, Jost, Blaise Thomson, and Steve Young. "Error simulation for training statistical dialogue systems." In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430167.

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Paliwal, Kuldip K., James G. Lyons, Stephen So, Anthony P. Stark, and Kamil K. Wojcicki. "Comparative evaluation of speech enhancement methods for robust automatic speech recognition." In 2010 4th International Conference on Signal Processing and Communication Systems (ICSPCS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icspcs.2010.5709761.

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Ney, H. "One decade of statistical machine translation: 1996-2005." In IEEE Workshop on Automatic Speech Recognition and Understanding, 2005. IEEE, 2005. http://dx.doi.org/10.1109/asru.2005.1566466.

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Junichi Tsujii. "Combining statistical models with symbolic grammar in parsing." In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430140.

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Reports on the topic "Automatic speech recognition – Statistical methods"

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Fatehifar, Mohsen, Josef Schlittenlacher, David Wong, and Kevin Munro. Applications Of Automatic Speech Recognition And Text-To-Speech Models To Detect Hearing Loss: A Scoping Review Protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2023. http://dx.doi.org/10.37766/inplasy2023.1.0029.

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Review question / Objective: This scoping review aims to identify published methods that have used automatic speech recognition or text-to-speech recognition technologies to detect hearing loss and report on their accuracy and limitations. Condition being studied: Hearing enables us to communicate with the surrounding world. According to reports by the World Health Organization, 1.5 billion suffer from some degree of hearing loss of which 430 million require medical attention. It is estimated that by 2050, 1 in every 4 people will experience some sort of hearing disability. Hearing loss can si

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