Relevant bibliographies by topics / Robust speech features

Academic literature on the topic 'Robust speech features'

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Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Robust speech features"

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Huang, Kuo-Chang, Yau-Tarng Juang, and Wen-Chieh Chang. "Robust integration for speech features." Signal Processing 86, no. 9 (September 2006): 2282–88. http://dx.doi.org/10.1016/j.sigpro.2005.10.020.

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Potamianos, Alexandros. "Novel features for robust speech recognition." Journal of the Acoustical Society of America 112, no. 5 (November 2002): 2278. http://dx.doi.org/10.1121/1.4779131.

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Goh, Yeh Huann, Paramesran Raveendran, and Sudhanshu Shekhar Jamuar. "Robust speech recognition using harmonic features." IET Signal Processing 8, no. 2 (April 2014): 167–75. http://dx.doi.org/10.1049/iet-spr.2013.0094.

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Eskikand, Parvin Zarei, and Seyyed Ali Seyyedsalehia. "Robust speech recognition by extracting invariant features." Procedia - Social and Behavioral Sciences 32 (2012): 230–37. http://dx.doi.org/10.1016/j.sbspro.2012.01.034.

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Dimitriadis, D., P. Maragos, and A. Potamianos. "Robust AM-FM features for speech recognition." IEEE Signal Processing Letters 12, no. 9 (September 2005): 621–24. http://dx.doi.org/10.1109/lsp.2005.853050.

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Harding, Philip, and Ben Milner. "Reconstruction-based speech enhancement from robust acoustic features." Speech Communication 75 (December 2015): 62–75. http://dx.doi.org/10.1016/j.specom.2015.09.011.

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Raj, Bhiksha, Michael L. Seltzer, and Richard M. Stern. "Reconstruction of missing features for robust speech recognition." Speech Communication 43, no. 4 (September 2004): 275–96. http://dx.doi.org/10.1016/j.specom.2004.03.007.

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ONOE, K., S. SATO, S. HOMMA, A. KOBAYASHI, T. IMAI, and T. TAKAGI. "Bi-Spectral Acoustic Features for Robust Speech Recognition." IEICE Transactions on Information and Systems E91-D, no. 3 (March 1, 2008): 631–34. http://dx.doi.org/10.1093/ietisy/e91-d.3.631.

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Bansal, Poonam, Amita Dev, and Shail Jain. "Robust Feature Vector Set Using Higher Order Autocorrelation Coefficients." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 4 (October 2010): 37–46. http://dx.doi.org/10.4018/ijcini.2010100103.

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In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower orders, while the higher-order autocorrelation coefficients are least affected, this method discards the lower order autocorrelation coefficients and uses only the higher-order autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-order autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further by the Mel filter bank; a log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the Differentiated Relative Higher Order Autocorrelation Coefficient Sequence Spectrum (DRHOASS). The authors evaluate the speech recognition performance of the DRHOASS features and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech.
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Majeed, Sayf A., Hafizah Husain, and Salina A. Samad. "Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition." Archives of Acoustics 40, no. 1 (March 1, 2015): 25–31. http://dx.doi.org/10.1515/aoa-2015-0004.

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Abstract In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions
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Dissertations / Theses on the topic "Robust speech features"

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Saenko, Ekaterina 1976. "Articulatory features for robust visual speech recognition." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28736.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 99-105).
This thesis explores a novel approach to visual speech modeling. Visual speech, or a sequence of images of the speaker's face, is traditionally viewed as a single stream of contiguous units, each corresponding to a phonetic segment. These units are defined heuristically by mapping several visually similar phonemes to one visual phoneme, sometimes referred to as a viseme. However, experimental evidence shows that phonetic models trained from visual data are not synchronous in time with acoustic phonetic models, indicating that visemes may not be the most natural building blocks of visual speech. Instead, we propose to model the visual signal in terms of the underlying articulatory features. This approach is a natural extension of feature-based modeling of acoustic speech, which has been shown to increase robustness of audio-based speech recognition systems. We start by exploring ways of defining visual articulatory features: first in a data-driven manner, using a large, multi-speaker visual speech corpus, and then in a knowledge-driven manner, using the rules of speech production. Based on these studies, we propose a set of articulatory features, and describe a computational framework for feature-based visual speech recognition. Multiple feature streams are detected in the input image sequence using Support Vector Machines, and then incorporated in a Dynamic Bayesian Network to obtain the final word hypothesis. Preliminary experiments show that our approach increases viseme classification rates in visually noisy conditions, and improves visual word recognition through feature-based context modeling.
by Ekaterina Saenko.
S.M.
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Domont, Xavier. "Hierarchical spectro-temporal features for robust speech recognition." Münster Verl.-Haus Monsenstein und Vannerdat, 2009. http://d-nb.info/1001282655/04.

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Javadi, Ailar. "Bio-inspired noise robust auditory features." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44801.

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The purpose of this work is to investigate a series of biologically inspired modifications to state-of-the-art Mel- frequency cepstral coefficients (MFCCs) that may improve automatic speech recognition results. We have provided recommendations to improve speech recognition results de- pending on signal-to-noise ratio levels of input signals. This work has been motivated by noise-robust auditory features (NRAF). In the feature extraction technique, after a signal is filtered using bandpass filters, a spatial derivative step is used to sharpen the results, followed by an envelope detector (recti- fication and smoothing) and down-sampling for each filter bank before being compressed. DCT is then applied to the results of all filter banks to produce features. The Hidden- Markov Model Toolkit (HTK) is used as the recognition back-end to perform speech recognition given the features we have extracted. In this work, we investigate the role of filter types, window size, spatial derivative, rectification types, smoothing, down- sampling and compression and compared the final results to state-of-the-art Mel-frequency cepstral coefficients (MFCC). A series of conclusions and insights are provided for each step of the process. The goal of this work has not been to outperform MFCCs; however, we have shown that by changing the compression type from log compression to 0.07 root compression we are able to outperform MFCCs for all noisy conditions.
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Schädler, Marc René [Verfasser]. "Robust automatic speech recognition and modeling of auditory discrimination experiments with auditory spectro-temporal features / Marc René Schädler." Oldenburg : BIS-Verlag, 2016. http://d-nb.info/1113296755/34.

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Jancovic, Peter. "Combination of multiple feature streams for robust speech recognition." Thesis, Queen's University Belfast, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268386.

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Fairhurst, Harry. "Robust feature extraction for the recognition of noisy speech." Thesis, University of Liverpool, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327705.

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Darch, Jonathan J. A. "Robust acoustic speech feature prediction from Mel frequency cepstral coefficients." Thesis, University of East Anglia, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445206.

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Szymanski, Lech. "Comb filter decomposition feature extraction for robust automatic speech recognition." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27051.

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This thesis discusses the issues of Automatic Speech Recognition in presence of additive white noise. Comb Filter Decomposition (CFD), a new method for approximating the magnitude of the speech spectrum in terms of its harmonics is proposed. Three feature extraction methods from CFD coefficients are introduced. The performance of the method and resulting features are evaluated using simulated recognition systems with Hidden Markov Model classifiers and conditions of additive white noise under varying Signal to Noise ratios. The results are compared with the performance of the existing robust feature extraction methods. The results show that the proposed method has a good potential for Automatic Speech Recognition under noisy conditions.
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Sklar, Alexander Gabriel. "Channel Modeling Applied to Robust Automatic Speech Recognition." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_theses/87.

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In automatic speech recognition systems (ASRs), training is a critical phase to the system?s success. Communication media, either analog (such as analog landline phones) or digital (VoIP) distort the speaker?s speech signal often in very complex ways: linear distortion occurs in all channels, either in the magnitude or phase spectrum. Non-linear but time-invariant distortion will always appear in all real systems. In digital systems we also have network effects which will produce packet losses and delays and repeated packets. Finally, one cannot really assert what path a signal will take, and so having error or distortion in between is almost a certainty. The channel introduces an acoustical mismatch between the speaker's signal and the trained data in the ASR, which results in poor recognition performance. The approach so far, has been to try to undo the havoc produced by the channels, i.e. compensate for the channel's behavior. In this thesis, we try to characterize the effects of different transmission media and use that as an inexpensive and repeatable way to train ASR systems.
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Mushtaq, Aleem. "An integrated approach to feature compensation combining particle filters and Hidden Markov Models for robust speech recognition." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/48982.

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The performance of automatic speech recognition systems often degrades in adverse conditions where there is a mismatch between training and testing conditions. This is true for most modern systems which employ Hidden Markov Models (HMMs) to decode speech utterances. One strategy is to map the distorted features back to clean speech features that correspond well to the features used for training of HMMs. This can be achieved by treating the noisy speech as the distorted version of the clean speech of interest. Under this framework, we can track and consequently extract the underlying clean speech from the noisy signal and use this derived signal to perform utterance recognition. Particle filter is a versatile tracking technique that can be used where often conventional techniques such as Kalman filter fall short. We propose a particle filters based algorithm to compensate the corrupted features according to an additive noise model incorporating both the statistics from clean speech HMMs and observed background noise to map noisy features back to clean speech features. Instead of using specific knowledge at the model and state levels from HMMs which is hard to estimate, we pool model states into clusters as side information. Since each cluster encompasses more statistics when compared to the original HMM states, there is a higher possibility that the newly formed probability density function at the cluster level can cover the underlying speech variation to generate appropriate particle filter samples for feature compensation. Additionally, a dynamic joint tracking framework to monitor the clean speech signal and noise simultaneously is also introduced to obtain good noise statistics. In this approach, the information available from clean speech tracking can be effectively used for noise estimation. The availability of dynamic noise information can enhance the robustness of the algorithm in case of large fluctuations in noise parameters within an utterance. Testing the proposed PF-based compensation scheme on the Aurora 2 connected digit recognition task, we achieve an error reduction of 12.15% from the best multi-condition trained models using this integrated PF-HMM framework to estimate the cluster-based HMM state sequence information. Finally, we extended the PFC framework and evaluated it on a large-vocabulary recognition task, and showed that PFC works well for large-vocabulary systems also.
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Books on the topic "Robust speech features"

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Rao, K. Sreenivasa. Robust Emotion Recognition using Spectral and Prosodic Features. New York, NY: Springer New York, 2013.

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Rao, K. Sreenivasa, and Shashidhar G. Koolagudi. Robust Emotion Recognition using Spectral and Prosodic Features. Springer, 2013.

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Rao, K. Sreenivasa, and Shashidhar G. Koolagudi. Robust Emotion Recognition using Spectral and Prosodic Features. Springer, 2013.

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Book chapters on the topic "Robust speech features"

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Buckow, Jan, Volker Warnke, Richard Huber, Anton Batliner, Elmar Nöth, and Heinrich Niemann. "Fast and Robust Features for Prosodic Classification?" In Text, Speech and Dialogue, 193–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48239-3_35.

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Manchala, Sadanandam, and V. Kamakshi Prasad. "GMM Based Language Identification System Using Robust Features." In Speech and Computer, 154–61. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01931-4_21.

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Schukat-Talamazzini, E. Günter. "Robust Features for Word Recognition." In Recent Advances in Speech Understanding and Dialog Systems, 291–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83476-9_28.

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Mihelič, France, and Janez Žibert. "Robust Speech Detection Based on Phoneme Recognition Features." In Text, Speech and Dialogue, 455–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846406_57.

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Missaoui, Ibrahim, and Zied Lachiri. "Gabor Filterbank Features for Robust Speech Recognition." In Lecture Notes in Computer Science, 665–71. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07998-1_76.

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Mitra, Vikramjit, Horacio Franco, Richard M. Stern, Julien van Hout, Luciana Ferrer, Martin Graciarena, Wen Wang, Dimitra Vergyri, Abeer Alwan, and John H. L. Hansen. "Robust Features in Deep-Learning-Based Speech Recognition." In New Era for Robust Speech Recognition, 187–217. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64680-0_8.

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Kovács, György, László Tóth, and Tamás Grósz. "Robust Multi-Band ASR Using Deep Neural Nets and Spectro-temporal Features." In Speech and Computer, 386–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11581-8_48.

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Ekpenyong, Moses E., Udoinyang G. Inyang, and Victor E. Ekong. "Intelligent Speech Features Mining for Robust Synthesis System Evaluation." In Human Language Technology. Challenges for Computer Science and Linguistics, 3–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93782-3_1.

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Alam, Md Jahangir, Patrick Kenny, and Douglas O’Shaughnessy. "Smoothed Nonlinear Energy Operator-Based Amplitude Modulation Features for Robust Speech Recognition." In Advances in Nonlinear Speech Processing, 168–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38847-7_22.

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Müller, Florian, and Alfred Mertins. "Robust Features for Speaker-Independent Speech Recognition Based on a Certain Class of Translation-Invariant Transformations." In Advances in Nonlinear Speech Processing, 111–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11509-7_15.

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Conference papers on the topic "Robust speech features"

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Kemp, Thomas, Climent Nadeu, Yin Hay Lam, and Josep Maria Sola i. Caros. "Environmental robust features for speech detection." In Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-349.

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Kristjansson, Trausti, Sabine Deligne, and Peder Olsen. "Voicing features for robust speech detection." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-186.

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Sam, Sethserey, Xiong Xiao, Laurent Besacier, Eric Castelli, Haizhou Li, and Eng Siong Chng. "Speech modulation features for robust nonnative speech accent detection." In Interspeech 2011. ISCA: ISCA, 2011. http://dx.doi.org/10.21437/interspeech.2011-629.

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Kelly, Finnian, and Naomi Harte. "Auditory Features Revisited for Robust Speech Recognition." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.1082.

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Mak, Brian, Yik-Cheung Tam, and Qi Li. "Discriminative auditory features for robust speech recognition." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5743734.

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Mak, Yik-Cheung Tam, and Qi Li. "Discriminative auditory features for robust speech recognition." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005756.

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Saenko, Kate, Trevor Darrell, and James R. Glass. "Articulatory features for robust visual speech recognition." In the 6th international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1027933.1027960.

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Sun, Zhaomang, Fei Zhou, and Qingmin Liao. "A robust feature descriptor based on multiple gradient-related features." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952388.

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Zha, Zhuan-ling, Jin Hu, Qing-ran Zhan, Ya-hui Shan, Xiang Xie, Jing Wang, and Hao-bo Cheng. "Robust speech recognition combining cepstral and articulatory features." In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322773.

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Drugman, Thomas, Yannis Stylianou, Langzhou Chen, Xie Chen, and Mark J. F. Gales. "Robust excitation-based features for Automatic Speech Recognition." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178855.

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Reports on the topic "Robust speech features"

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Nahamoo, David. Robust Models and Features for Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada344834.

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