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Dissertations / Theses on the topic 'Robust speech features'

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Published: 4 June 2021
Last updated: 11 February 2022

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1

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.<br>Includes bibliographical references (p. 99-105).<br>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
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2

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
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4

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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5

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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6

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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7

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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8

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 f
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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
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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 spee
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11

Herms, Robert [Verfasser], Maximilian [Akademischer Betreuer] Eibl, Maximilian [Gutachter] Eibl, and Günter Daniel [Gutachter] Rey. "Effective Speech Features for Cognitive Load Assessment: Classification and Regression / Robert Herms ; Gutachter: Maximilian Eibl, Günter Daniel Rey ; Betreuer: Maximilian Eibl." Chemnitz : Universitätsverlag Chemnitz, 2019. http://d-nb.info/1215909594/34.

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12

Shao, Yang. "Sequential organization in computational auditory scene analysis." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1190127412.

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13

Suryanarayana, Venkata K. "Spectro-Temporal Features For Robust Automatic Speech Recognition." Thesis, 2009. http://hdl.handle.net/2005/1007.

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The speech signal is inherently characterized by its variations in time, which get reflected as variations in frequency. The specto temporal changes are due to changes in vocaltract, intonation, co-articulation and successive articulation of different phonetic sounds. In this thesis we are looking for improving the speech recognition performance through better feature parameters using a non-stationary model of speech. One effective means of modeling a general non-stationary signal is using the AM-FM model. AM-FM model can be extended to speech through a sub-band analysis, which can be mimic th
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14

Hsien-ShunKuo and 郭先舜. "Auditory-Based Features for Robust Speech Recognition System." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/17104347666389915184.

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碩士<br>國立成功大學<br>電機工程學系<br>102<br>An auditory-based feature extraction algorithm is proposed for enhancing the robustness of automatic speech recognition. In the proposed approach, the speech signal is characterized using a new feature referred to as the Basilar-membrane Frequency-band Cepstral Coefficient (BFCC). In contrast to the conventional Mel-Frequency Cepstral Coefficient (MFCC) method based on a Fourier spectrogram, the proposed BFCC method uses an auditory spectrogram based on a gammachirp wavelet transform in order to more accurately mimic the auditory response of the human ear and i
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Tu, Wen-Hsiang, and 杜文祥. "Enhancing Speech Features in Various Domains for Noise-Robust Speech Recognition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/66756772463135462510.

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博士<br>國立暨南國際大學<br>電機工程學系<br>100<br>The performance of an automatic speech recognition (ASR) system is often degraded due to the various types of noise and interference in the application environment. In this disseration, we aim to develop robustness methods specifically for handling additive noise and channel disturbance. In particular, these developed methods are used to refine the mel-frequency cepstral coefficient (MFCC), which is one of the most widely used speech feature representation in ASR. At first, we discuss the effect of noise in the linear spectral domain of MFCC, and then pr
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Fan, Hao-Teng, and 范顥騰. "Sub-band Processing in Various Domains of Speech Features for Robust Speech Recognition." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/15896893826936012585.

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博士<br>國立暨南國際大學<br>電機工程學系<br>102<br>The environmental mismatch caused by additive noise and/or channel distortion often dramatically degrades the performance of an automatic speech recognition system (ASR). In order to reduce this mismatch, a plenty of robustness techniques have been developed.This dissertation proposes several novel methods via using sub-band process in different domains of speech features to improve noise robustness for speech recognition. Briefly speaking, in this dissertation we investigate the noise effect in three domains of speech features and then develop the respective
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Lin, Wen-chi, and 林文琦. "DCT-based Processing of Dynamic Features for Robust Speech Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/32454204118543401202.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>98<br>In this thesis, we explore the various properties of cepstral time coefficients (CTC) in speech recognition, and then propose several methods to refine the CTC construction process. It is found that CTC are the filtered version of mel-frequency cepstral coefficients (MFCC), and the used filters are from the discrete cosine transform (DCT) matrix. We modify these DCT-based filters by windowing, removing DC gain, and varying the filter length. The speech recognition task using Aurora-2 digit database show that the proposed methods can enhance the original CTC in
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18

Ion, Valentin [Verfasser]. "Transmission error robust speech recognition using soft features / von Valentin Ion." 2008. http://d-nb.info/990334589/34.

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Ni-Chun, Wang, and 王迺鈞. "Multi-eigenvector-based Features and Related Topics for Robust Speech Recognition." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/01537864568510620829.

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Hsieh, Hsin-Ju, and 謝欣汝. "Compensating the speech features in the temporal domain via discrete cosine transform for robust speech recognition." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/00106248218554232326.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>99<br>In this thesis, we develop a series of algorithms to improve the noise robustness of speech features based on discrete cosine transform (DCT). The DCT-based modulation spectra of clean speech feature streams in the training set are employed to generate two sequences representing the reference magnitudes and magnitude weights, respectively. The two sequences are then used to update the magnitude spectrum of each feature stream in the training and testing sets. The resulting new feature streams have shown robustness against the noise distortion. The experiments
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21

Lu, I.-Chia, and 呂宜家. "Exploiting wavelet de-noising in the temporal sequencesof features for robust speech recognition." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/03773923951424999965.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>99<br>In this thesis, we propose to apply the wavelet de-noising (WD) techniques in temporal-domain feature sequences for enhancing the noise robustness in order to improve the accuracy of noisy speech recognition. In the proposed method, the temporal domain feature sequence is first processed by some specific statistic normalization scheme, such as mean and variance normalization (MVN) and cepstral gain normalization(CGN), and then dealt with the wavelet de-noising algorithm. With this process, we find that the wavelet de-noising procedure can effectively reduce th
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"Exploitation of phase and vocal excitation modulation features for robust speaker recognition." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075192.

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Mel-frequency cepstral coefficients (MFCCs) are widely adopted in speech recognition as well as speaker recognition applications. They are extracted to primarily characterize the spectral envelope of a quasi-stationary speech segment. It was shown that cepstral features are closely related to the linguistic content of speech. Besides the magnitude-based cepstral features, there are resources in speech, e.g, the phase and excitation source, are believed to contain useful properties for speaker discrimination. Moreover, in real situations, there are large variations exist between the development
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Pan, Chi-an, and 潘吉安. "Study of the Improved Normalization Techniques of Energy-Related Features for Robust Speech Recognition." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/04279772087665285401.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>96<br>The rapid development of speech processing techniques has made themselves successfully applied in more and more applications, such as automatic dialing, voice-based information retrieval, and identity authentication. However, some unexpected variations in speech signals deteriorate the performance of a speech processing system, and thus relatively limit its application range. Among these variations, the environmental mismatch caused by the embedded noise in the speech signal is the major concern of this thesis. In this thesis, we provide a more rigorous mathem
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"Robust speaker recognition using both vocal source and vocal tract features estimated from noisy input utterances." 2007. http://library.cuhk.edu.hk/record=b5893317.

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Wang, Ning.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.<br>Includes bibliographical references (leaves 106-115).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Introduction to Speech and Speaker Recognition --- p.1<br>Chapter 1.2 --- Difficulties and Challenges of Speaker Authentication --- p.6<br>Chapter 1.3 --- Objectives and Thesis Outline --- p.7<br>Chapter 2 --- Speaker Recognition System --- p.10<br>Chapter 2.1 --- Baseline Speaker Recognition System Overview --- p.10<br>Chapter 2.1.1 --- Feature Extraction --- p.12<br
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WANG, SHANG-YU, and 王上瑜. "A Study of Applying Noise-Robust Features in Reduced Frame-Rate Acoustic Models for Speech Recognition." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/63485710426800421992.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>104<br>Speech recognition in mobile devices has been increasingly popular in our life, while it has to deal with the requirements of high recognition accuracy and low transmission load. One of the most challenging tasks for improving the recognition accuracy for real-world applications is to alleviate the noise effect, and one prominent way to reducing the transmission load is to make the speech features as compact as possible. In this study, we evaluate and explore the effectiveness of integrating the noise-robust speech feature representation with the reduced fram
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Kao, Shyh-Jer, and 高世哲. "Robust Speech Recognition Based on Feature Normalization." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/42377056084343012440.

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碩士<br>國立交通大學<br>電信工程系所<br>95<br>In this thesis. Some robust speech feature processing algorithms were proposed, in order to improve the speech recognition performance under the noisy environments . First, the well-known robust speech feature processing algorithms such as mean variance normalization(MVN) and histogram equalization(HEQ) was implemented in a Mandarin AURORA-like system database as the base-line system. Then, the class-based MVA was proposed to further implement the speech recognition performance. The class-based MVA algorithm was first categorized the signal into speech and non-s
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Huang, Yung-Sheng, and 黃永勝. "Robust Speech Recognition : Improved temporal filtering on speech feature coefficients." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/59595195142262613727.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>92<br>In recent year, with the fast expanding of high-tech industry, computer is much important for human being. For the convenient communication to computer, Man Machine Interface of speech recognition has become one of the most important researches in the world and also has been applied in daily life. However, the most important problem of speech recognition techniques is the accuracy of recognition. But, when there is a mismatch between the acoustic conditions of training and application environments for a speech recognition system, the performance of the system
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Lin, Meng-kai, and 林盟凱. "Feature Exponent Adjustment Methods in Robust Speech Recognition." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/51556065034316819061.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>94<br>The performance of a speech recognition system is often degraded due to the mismatch between the environments of development and application. One of the major sources that give rise to this mismatch is, additive noise. The approaches for handling the problem of additive noise can be divided into three classes, speech enhancement, robust representation of speech, and compensation of speech models. In this thesis, the discussed methods belong to the first class, speech enhancement techniques. A common characteristic of our studied and proposed approaches in
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陳韋豪. "Feature Normalization Exploiting Spatial-Temporal Distribution Characteristics for Robust Speech Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/01407714632726246776.

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Hsieh, Tsung-Hsueh, and 謝宗學. "Feature Statistics Compensation for Robust Speech Recognition in Additive Noise Environments." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/77143721882774978160.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>95<br>To improve the accuracy of a speech recognition system under a mismatched noisy environment has always been a major research issue in the speech processing area. A great amount of approaches have been proposed to reduce this environmental mismatch, and one class of these approaches focuses on normalizing the statistics of speech features under different noise conditions. The well-known utterance-based cepstral mean and variance normalization (U-CMVN) and segmental cepstral mean and variance normalization (S-CMVN) both belong to this class. Both of them make us
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Chu, Po-Han, and 祝伯翰. "Front-End Feature Processing using Particle Filter for Robust Speech Recognition." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/56355259808505297847.

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Wu, Szyu, and 吳思予. "The study of speech feature shaping and normalization in quefrency bands for noise-robust speech recognition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/53851035103957265947.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>100<br>In this study, we develop a novel noise-robustness method, termed weighted sub-band level histogram equalization (WS-HEQ), to promote the speech recognition accuracy in a noise-corrupted environment. Based on the observation that the high-pass and low-pass portions of the intra-frame cepstral features possess unequal importance for speech recognition and different signal-to-noise ratios (SNRs), WS-HEQ intends to alleviate the high-pass portion in order to highlight the speech components and reduce the effect of noise. Furthermore, we provide four variants of
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Tu, Wen Hsiang, and 杜文祥. "Study on the Voice Activity Detection Techniques for Robust Speech Feature Extraction." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/76966247400637028949.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>95<br>The performance of a speech recognition system is often degraded due to the mismatch between the environments of development and application. One of the major sources that give rises to this mismatch is additive noise. The approaches for handling the problem of additive noise can be divided into three classes: speech enhancement, robust speech feature extraction, and compensation of speech models. In this thesis, we are focused on the second class, robust speech feature extraction. The approaches of speech robust feature extraction are often together with the
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34

Tseng, Wen-Yu, and 曾文俞. "Linear Prediction Processing of Feature Time Sequences for Noise-Robust Speech Recognition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/99175156112060279105.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>101<br>This paper presents a novel method for extracting noise-robust speech features in speech recognition. In the presented method, the algorithm of linear predictive coding (LPC) is exploited on the feature time series of mel-frequency cepstral coefficients (MFCC), and the resulting linear predictive version of the feature time series are shown to be more noise-robust than the original one, with the probable reason that the prediction error component that corresponds to noise effect is reduced. The process of LPC in the presented method is analogous to a tempora
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Kao, Yu-chen, and 高予真. "Distribution-based Feature Normalization with Temporal-Structural Information on Robust Speech Recognition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/27w69n.

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碩士<br>國立臺灣師範大學<br>資訊工程學系<br>101<br>Recently, histogram equalization (HEQ) of speech features has received considerable attention in the area of robust speech recognition because of its relative simplicity and good empirical performance. In this thesis, we present a polynomial variant of spectral histogram equalization (SHE) on the modulation spectra of speech features and a novel extension to the conventional HEQ approach conducted on the cepstral domain. Our HEQ methods at least have the following two attractive properties. First, polynomial regression of various orders is employed to efficie
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Chiou, Sheng-chiuan, and 邱聖權. "Auditory Based Modification of MFCC Feature Extraction for Robust Automatic Speech Recognition." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/9qrexg.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>97<br>The human auditory perception system is much more noise-robust than any state-of theart automatic speech recognition (ASR) system. It is expected that the noise-robustness of speech feature vectors may be improved by employing more human auditory functions in the feature extraction procedure. Forward masking is a phenomenon of human auditory perception, that a weaker sound is masked by the preceding stronger masker. In this work, two human auditory mechanisms, synaptic adaptation and temporal integration are implemented by filter functions and incorporated to
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張志豪. "Robust And Discriminative Feature Extraction Techniques For Large Vocabulary Continuous Speech Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/90526888062025408931.

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碩士<br>國立臺灣師範大學<br>資訊工程研究所<br>93<br>Speech is the primary and the most convenient means of communication between people. Due to the successful development of much smaller electronic devices and the popularity of wireless communication and networking, it is widely believed that speech will play a more active role and will serve as the major human-machine interface for the interaction between people and different kinds of smart devices in the near future. Therefore, research on automatic speech recognition (ASR) is now becoming more and more emphasized, and in which the development of discriminat
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Tsai, Shang-nien, and 蔡尚年. "Robust Speech Feature Front-End Processing Techniques Based on Progressive Histogram Equalization." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/70500526488219371940.

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Yeh, Bing-Feng, and 葉秉豐. "Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech Recognition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/69780960016247960787.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>100<br>In this paper, we propose a new method for noise robustness base on Gaussian Mixture Model (GMM), and the method we proposed can estimate the noise feature effectively and reduce noise effect by plain fashion, and we can retain the smoothing and continuity from original feature in this way. Compared to the traditional feature transformation method MMSE(Minimum Mean Square Error) which want to find a clean one, the different is that the method we proposed only need to fine noise feature or the margin of noise effect and subtract the noise to achieve more robu
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Tai, Chung-Fu, and 戴仲甫. "The Improved Techniques of Energy Feature Enhancement and FrameSelection for Robust Speech Recognition." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/28763460961441249370.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>94<br>The performance of a speech recognition system is often seriously degraded in the presence of noise. In this thesis, the problem of additive noise is the main issue and frame selectivity techniques and some compensation approaches on the energy feature are analyzed to improve the recognition accuracy. A common advantage of these methods is its low computation complexity. They help to separate the non-speech portion from the whole utterance, and thus the recognition speed can be further improved. In chapter 3, we discuss the methods that compensate all cepstra
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Cheng-Wei, Liu, and 劉成韋. "A Study on Feature Normalization and Other Improved Techniques for Robust Speech Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/45415359257552707820.

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碩士<br>國立臺灣師範大學<br>資訊工程研究所<br>93<br>In the course of evolution for thousands of years, human beings have continuously acquired as well as accumulated their knowledge from their daily life. Therefore, the civilization and evolution of human beings were almost on a par with each other in the past several thousand years. However, the quick development of technology nowadays has surmounted the evolution of human beings further. For example, huge quantities of multimedia information, such as broadcast radio and television programs, voice mails, digital archives and so on, are continuously growing an
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Chang, Yang, and 張暘. "Robust Speech Recognition with Two-dimensional Frame-and-feature Weighting and Modulation Spectrum Normalization." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/80127356350852988068.

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碩士<br>國立臺灣大學<br>電信工程學研究所<br>100<br>In this paper we propose a new approach of two-dimensional frame-and-feature weighted Viterbi decoding performed at the recognizer back-end for robust speech recognition. The frame weighting is based on an Support Vector Machine (SVM) classifier considering the energy distribution and cross-correlation spectrum of the frame. The basic idea is that voiced frames with higher harmonicity is in general more reliable than other frames in noisy speech and therefore should be weighted higher. The feature weighting is based on an entropy measure considering confusion
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Liang, Cheng-hao, and 梁振浩. "Vehicle Distance Estimation Using Optical Flow and Speed Up Robust Feature." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/28666570965231154217.

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碩士<br>國立中央大學<br>資訊工程學系<br>103<br>Because effect of accident rate rising on the highway, making vehicle anti-collision system as the current main trends. Moreover, currently vehicle anti-collision system is also widely used in unmanned aerial vehicles. Such as Apple, Benz, BMW, Audi, etc. Among Google Driverless Car as a representative. In recent years, most of the vehicle anti-collision system with sensors to prevent collisions. The reason the current prices of sensors are still expensive and the consumer demand of people is not high. Then, making low-cost vehicle anti-collision system to be v
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Fan, Hao-teng, and 范顥騰. "The Study of Sub-band Feature Statistics Compensation Techniques Based on a Discrete Wavelet Transform for Robust Speech Recognition." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/24264693292357990593.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>97<br>The environmental mismatch caused by additive noise and/or channel distortion often degrades the performance of a speech recognition system seriously. Various robustness techniques have been proposed to reduce this mismatch, and one category of them aims to normalize the statistics of speech features in both training and testing conditions. In general, these statistics normalization methods deal with the speech feature sequences in a full-band manner, which somewhat ignores the fact that different modulation frequency components have unequal importance for spe
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Chao-chieh, CHEN, and 陳兆捷. "Design and Implementation of Mandarin Robot Speech Recognition System using Linear Transient/Steady-State Feature Coefficient Alignment." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/03533150119221902400.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>97<br>This thesis design and implement of a robotic Mandarin speech recognition system.Due to the reason that the commonly used conventional robot speech recognition system “Dynamic Time Warping model (DTW)” requires an extensive amount of calculation and processing, we proposed and tested a novel speech recognition design“Linear Transient/Steady-State Feature Coefficient Alignment model (LTSFCA)” Relying on the basis of Chinese language pronunciations which separates speeches into transient and Steady-State segments, this method fractionates speeches into pieces for
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