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33rd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： McDonald, Rob Parsa, Vijay Doyle, Philip Chen, Guo Univ Western Ontario Sch Commun Sci & Disorders London ON Canada Univ Western Ontario Dept Elect & Comp Engn London ON Canada Univ Ottawa Sch Informat Technol & Engn Ottawa ON Canada

ISBN: (纸本)9781424414833

Total laryngectomy is often the treatment of choice for many patients suffering from laryngeal cancer. This procedure alters the speech production mechanism, and tracheoesophageal (TE) speech is an alternative where the pulmonary air is forced through the esophagus. TE speech is often characterized by poor intelligibility and voice quality. Acoustic analysis of TE speech has the potential of quantifying the voice quality and assisting the speech pathologist in determining and monitoring the therapy process. In this paper, we apply two different methods for predicting the voice quality ratings of TE speakers by naive listeners: (a) conventional spectral and linear prediction measurements that were investigated in earlier studies, and (b) a methodology based on a perceptual auditory model that attempts to mimic the speech quality perception by a normal hearing listener. Experimental results with a database of 35 TE speakers showed that the auditory-model based approach significantly outperforms the traditional methods.

关键词： vocal system speech analysis linear prediction coding auditory system

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Proposing Optimal ARMA Based Model for Measurement Compensation in the State Estimation 15

Proposing Optimal ARMA Based Model for Measurement Compensat...

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15th International Conference on Frontiers of Information Technology (FIT)

作者： Bacha, Syed Abuzar Khan, Naeem UET Peshawar Dept Elect Engn Bannu Campus Peshawar Pakistan

ISBN: (纸本)9781538635674

The purpose of this paper is to improve state estimation in the event of data loss by augmenting a novel Moving Average Autoregressive-based artificial measurement vector with Kalman filtering. The proposed technique replaces the existing Autoregressive-series based model embedded in the linear prediction techniques through Moving Average Autoregressive-based model. The Autoregressive scheme needs only one type of linear prediction coefficient to be tracked, while the proposed scheme computes two parameters at each recursion. Since Autoregressive Moving Average technique possesses more information, hence it efficiently predicts the future values of a signal. This value is placed as an alternative in the structure (or steps) involved in standard process of state estimations. The ultimate consequences of this extra computations involve more computational efforts. A standard mass-spring damper case study has been provided to show some aspects of the existing and proposed techniques.

关键词： Autoregressive Kalman filter linear prediction coding

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Large Particle Reduction by Porous Membrane Filter 11

Large Particle Reduction by Porous Membrane Filter

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International Conference on Planarization CMP Technology (ICPT)

作者： Sumiya, Masato Nihon Pall Ltd SLS Global Tech Support Ami Ibaraki Japan

ISBN: (纸本)9781479955565

The objective of this study is to propose a laboratory test method for confirming LPC (large particle count) reduction by porous membrane filters, which are generally thought to have higher particle removal performance than melt-blown filters. CMP slurries were filtered by porous membrane filters and a nonwoven depth filter. Then LPC reduction of the filtrates was estimated by a particle counter in conjunction with control membrane test, in which pressure drop increase of a filter (a control membrane) was an indicator of LPC. Difference of LPC for filtrates from each type of filter was small by the particle counter and significant by the control membrane method, but with the porous membrane filter exhibiting lower LPC by the both methods. The results demonstrate that a combination of particle counter and control membrane method can reveal difference of filter performance in LPC reduction with confidence. Further, to show pressure drop increase could be an indicator of LPC, pressure drop increase of the control membrane enhanced by large particles was examined.

关键词： filtration materials testing particle counting porous materials CMP slurries LPC indicator LPC reduction control membrane method control membrane test filter performance filtrates laboratory test method large particle count reduction large particle reduction melt-blown filters nonwoven depth filter particle counter particle removal performance porous membrane filter pressure drop increase Aggregates Atmospheric measurements Filtration Radiation detectors Silicon compounds Slurries Suspensions particle counter Filtration Filterability linear prediction coding PCSK7 gene atmospheric monitoring Radiation detectors Silicon Compounds SLURRIES materials testing Filters porous membrane Pressure decline Porous materials Test Method

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Improved Silence-Unvoiced-Voiced (SUV) Segmentation for Dysarthric Speech Signals using linear prediction Error Variance 5

Improved Silence-Unvoiced-Voiced (SUV) Segmentation for Dysa...

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IEEE 5th International Conference on Computer and Communication Systems (ICCCS)

作者： Ijitona, Tolulope Yue, Hong Soraghan, John Lowit, Anja Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland Univ Strathclyde Sch Psychol Sci & Hlth Glasgow Lanark Scotland

ISBN: (纸本)9781728161365

A novel algorithm for the segmentation of dysarthric speech into silence, unvoiced and voiced (SUV) segments is presented. The proposed algorithm is based on the combination of short-time energy (STE), zero-crossing rate (ZCR) and linear prediction error variance (LPEV) or the segmentation problem. Extending the previous work in this field, the proposed method will address the difficulties in distinguishing between voiced and unvoiced segments in dysarthric speech. More precisely, the error variance of the linear prediction coefficients will be used to design a three-fold decision matrix that can accommodate the high variability in loudness experienced in dysarthric speech. In addition, a moving average threshold approach will be proposed in order to provide an "as-fit" segmentation technique that is fully automated and that will be able to handle highly severe dysarthric speech with varying loudness and ZCRs. The ability of the proposed fully-automated algorithm will be validated using real speech samples from healthy speakers, and speakers with ataxic dysarthria. The results of the proposed approach are compared with known methods using STE and ZCR. It is observed that the proposed classification method does not only show an improvement in segmentation performance but also provides consistent results in low signal energy situations.

关键词： Dysarthria Speech Disorder linear prediction Error Variance Zero Crossing Rate SUV Voiced-Unvoiced linear prediction coding

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Sparse linear prediction Coefficients for Isolated Speech Recognition

Sparse Linear Prediction Coefficients for Isolated Speech Re...

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International Conference on Control Communication & Computing India (ICCC)

作者： Ramitha, R. S. Baburaj, M. George, Sudhish N. Natl Inst Technol Dept Elect & Commun Engn Calicut 673601 Kerala India Govt Coll Engn Kannur Dept Elect & Commun Engn Mangattuparamba 670563 Kannur India

ISBN: (纸本)9781467373494

Isolated speech recognition system is an important step in many applications such as automated banking system, catalogue dialing, automated data entry, robotics etc. Selection of feature and classifier in the speech recognition system is based on the complexity and recognition accuracy. Mel-frequency cepstral coefficients (MFCCs), line spectral frequencies (LSF), short time energy (STE) and linear prediction coefficients (LPC) are the features used in the existing speech recognition systems. In this paper, a sparse feature, obtained from the optimization of linear prediction coefficients (LPC) with a sparsity constraint is used for the classification. These sparse linear prediction coefficients (sparse LPC) offer a more effective way of representing the voiced speech. Artificial neural network (ANN) is used for the classification purpose. Experimental results show that the proposed method is noise robust and its performance exceeds LPC and MFCC feature based speech recognition systems.

关键词： speech recognition system linear prediction PCSK7 gene linear prediction coding Speech recognition Mel frequency cepstral coefficient Noise robustness

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Packet Loss Concealment for VoIP Based on Pitch Waveform Replication and linear Predictive coding

Packet Loss Concealment for VoIP Based on Pitch Waveform Rep...

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IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

作者： Toyoshima, Masahiro Shimamura, Tetsuya Saitama Univ Grad Sch Sci & Engn Sakura Ku Saitama 3388570 Japan

ISBN: (纸本)9781479952304

In this paper, we propose a Packet Loss Concealment (PLC) method. Our method is based on Pitch Waveform Replication (PWR) and linear Predictive coding (LPC). The estimated packet using LPC is better than that using PWR at a near boundary of the lost packet and the received one. On the other hand, the estimated packet using PWR is better than that using LPC at a distant boundary. Therefore, we combine the two estimated packets by considering the merits and demerits of PWR and LPC. Experimental results show that the proposed method provides better Perceptual Evaluation of Speech Quality (PESQ) scores than the conventional methods. Especially PESQ scores of the proposed method are remarkably excellent in the case of male voice.

关键词： Internet telephony linear codes speech coding LPC PESQ scores PWR VoIP linear predictive coding packet loss concealment pitch waveform replication speech quality Delays Internet Jitter linear predictive coding Packet loss Speech linear prediction coding Packet loss PCSK7 gene Voice over Internet Protocol Internet telephony speech coding linear codes PRESSURIZED WATER REACTOR Jitter PESQ Speech Internets

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Interacting with computer using ears and tongue

Interacting with computer using ears and tongue

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4th IFIP International Conference on Intelligent Information Processing

作者： Shrawankar, Urmila Mahajan, Anjali Govt Polytech Inst Dept Informat Technol Nagpur 440001 Maharashtra India Raisoni Coll Engg Dept Comp Sci & Engg Nagpur 440016 Maharashtra India

ISBN: (纸本)0387446397

Human computer interaction is concerned in the way Users (humans) interact with the computers. Some users can interact with the computer using the traditional methods of a keyboard and mouse as the main input devices and the monitor as the main output device. Due to one or another reason, some users are enable to interact with machines using a mouse and keyboard device, hence there is need for special devices. If we use computer for more time it is really difficult to sit on the chair, keeping hands continuously on the keyboard or mouse and keep watching the monitor. To relax our body and interact comfortably with Computer, we need some special device or method, so that computer Will understand and accept commands without keyboard or by clicking mouse. Speech Recognition System helps users who are unable to use traditional Input and Output (I/O) devices. Since four decades, man has been dreaming for an "intelligent machine" which can master the natural speech. In its simplest form, this machine should consist of two subsystems, namely Automatic Speech Recognition (ASR) and Speech Understanding (SU). The goal of ASR is to transcribe natural speech while SU is to understand the meaning of the transcription. Recognising and understanding a spoken sentence is obviously a knowledge-intensive process, which must take into account all variable information about the speech communication process, from acoustics to semantics and pragmatics.

关键词： Automatic Speech Recognition text-to-speech speech-to-text interactive voice response-systems linear prediction coding hidden Markov model

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Fault diagnosis of rolling bearing based on PSO and continuous Gaussian mixture HMM

Fault diagnosis of rolling bearing based on PSO and continuo...

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2015 2nd International Conference on Machinery,Materials Engineering,Chemical Engineering and Biotechnology(MMECEB 2015)

作者： Liao Guangchun Zhu Haiping Liu Kangjun Liao Jiawei Huazhong University of Science & Technology

ISBN: (纸本)9781510819092

As the hidden Markov model(HMM) has a strong ability of time sequence modeling,the continuous Gaussian mixture HMM is used to establish a model base of the rolling bearing *** adaptive particle swarm optimization(APSO) with extremum disturbed operator and dynamic change of inertia weights is introduced to the traditional training algorithm for solving the local extremum *** vibration signal is collected for extracting 12 order LPC coefficients as a feature vector through the dispose of adding *** the given feature vector,the HMM is built for bearing fault condition monitoring and fault ***,different fault conditions experiment are carried out on the motor bearing *** experiment result shows that the method can use a small amount of samples for training HMM,and it is more effective and has higher classification accuracy in fault diagnosis compared with the traditional training algorithm.

关键词： Particle Swarm Optimization Hidden Markov Model Fault diagnosis LPC Hidden Markov models Fault diagnosis PCSK7 gene linear prediction coding Particle swarm optimization rolling bearings Eigenvector parametric subharmonic oscillator extremum values

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Gesture recognition using HLAC features of PARCOR images

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2003年第4期E86D卷 719-726页

作者： Kurita, T Hayamizu, S Natl Inst Ind Sci & Technol Neurosci Res Inst Tsukuba Ibaraki 3058568 Japan Gifu Univ Gifu 5011193 Japan

This paper proposes a gesture recognition method which uses higher order local autocorrelation (HLAC) features extracted from PARCOR images. To extract dominant information from a sequence of images, we apply linear prediction coding technique to the sequence of pixel intensities and PARCOR images are constructed from the PARCOR coefficients of the sequences of the pixel values. From the PARCOR images, HLAC features are extracted and the sequences of the features are used as the input vectors of the Hidden Markov Model (HMM) based recognizer. Since HLAC features are inherently shift-invariant and computationally inexpensive, the proposed method becomes robust to changes in the person's position and makes real-time gesture recognition possible. Experimental results of gesture recognition are shown to evaluate the performance of the proposed method.

关键词： gesture recognition PARCOR images Hidden Markov Model higher order local autocorrelation linear prediction coding

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Robust speech filtering in impulsive noise environments

Robust speech filtering in impulsive noise environments

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作者： Ledoux, Christelle Michelle Virginia Tech | University

This thesis presents a new robust filtering technique that suppresses impulsive noise in speech signals. The method makes use of Projection Statistics based on medians to detect segments of speech with impulses. The autoregressive model employed to smooth out the speech signal is identified by means of a robust nonlinear estimator known as the Schweppe-type Huber GM-estimator. Simulation results are presented that demonstrate the effectiveness of the filter. Another contribution of the work is the development of a robust version of the Kalman filter based on the Huber M-estimator. The performances of this filter are evaluated for a simple autoregressive process.

关键词： linear prediction coding robust statistics speech processing Thesis

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