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检索条件"主题词=Linear predictive coding"

共 2474 条记录，以下是371-380 订阅

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Enhanced Speech Recognition with Blind Equalization For Robot "WEVER-R2"

Enhanced Speech Recognition with Blind Equalization For Robo...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Donglin Wang Henry Leung Keun-Chang Kwak Hosub Yoon Department ofElectrical and Computer Engineeing University of Calgary AB Canada ETRI Daejeon South Korea

Ambient noise caused by room acoustics usually degrades quality and reliability of speech recognition for the service robot application. In this paper, we propose enhancing the robustness of speech recognition for home service robots by combining blind equalization with classification. In particular, the linear predictive code (LPC) is used for feature extraction. The constant modulus algorithm (CMA) is combined with the radial basis function (RBF) neural network to form a robust classifier with automatic equalization capability to reduce the room service effect. Using real speech data collected by WEVER-R2 acoustic robot, it is shown that the proposed method can increase the speech recognition rate from 74.46% to 87.23% for age recognition and from 80.95% to 95.09% for gender recognition.

关键词： Speech recognition Blind equalizers Service robots Automatic speech recognition Acoustic applications Degradation Noise robustness linear predictive coding Feature extraction Neural networks

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Use of Novel Feature Extraction Technique with Subspace Classifiers for Speech Recognition

Use of Novel Feature Extraction Technique with Subspace Clas...

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IEEE/ACS International Conference on Pervasive Services (ICPS)

作者： Serkan Gunal Rifat Edizkan Department of Computer Engineering Anadolu University Eskisehir Turkiye Department of Electrical and Electronics Engineering Eskisehir Osmangazi University Eskisehir Turkiye

Speech recognition is one of the fast moving research areas in pervasive services requiring human interaction. Like any type of pattern recognition system, selection of the feature extraction method and the classifier play a crucial role for speech recognition in terms of accuracy and speed. In this paper, an efficient wavelet based feature extraction method for speech data is presented. The feature vectors are then fed into three widely used linear subspace classifiers for recognition analysis. These classifiers are Class Featuring Information Compression (CLAFIC), Multiple Similarity Method (MSM) and Common Vector Approach (CVA). TI-DIGIT database is used to evaluate the performance of speaker independent isolated word recognition system designed. Experimental results indicate that the proposed feature extraction method together with the CLAFIC and CVA classifiers give considerably high recognition rates.

关键词： Feature extraction Speech recognition Continuous wavelet transforms Fourier transforms Wavelet transforms Wavelet analysis Humans linear predictive coding Mel frequency cepstral coefficient Data engineering

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A Fixed Dimension Modified Sinusoid Model (FD-MSM) for Single Microphone Sound Separation

A Fixed Dimension Modified Sinusoid Model (FD-MSM) for Singl...

引用

IEEE International Conference on Signal Processing and Communications, ICSPC

作者： Pejman Mowlaee Begzade Mahale Abolghasem Sayadiyan Alireza Bayesteh Tashk Department of Electrical Engineering Amirkabir University of Technology슠 Tehran Iran

The lack of a flexible analysis model has been introduced as an important issue in different applications like source separation. In this paper, a fixed dimension modified sinusoid model (FD-MSM) is proposed for analysis of all audible signals consisting of speech, music and their mixtures. Employing the peak picking in Meldomain gives rise to a fixed number of parameters in the proposed FDMSM, which is desired in clustering algorithms like VQ (vector quantization) or GMM (gaussian mixture model), commonly used for source separation scenarios. Applying the proposed FD-MSM to various audible signals, it is observed that the resulting signal is perceptually indistinguishable from the original.

关键词： Microphones Speech analysis Signal analysis Source separation Speech synthesis Multiple signal classification Vector quantization linear predictive coding Power harmonic filters Computational complexity

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Multi-agent Based Arabic Speech Recognition

Multi-agent Based Arabic Speech Recognition

引用

Web Intelligence and Intelligent Agent Technology Workshops (WI-IAT)

作者： Muhammad Taha Tarek Helmy Reda Abo Alez Faculty of Science Mathematical Department Cairo University Cairo Egypt College of Computer Science and Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Faculty of Engineering Computers & Systems Engneering Department Al-Azhar University Cairo Egypt

This paper presents a novel agent-based design for Arabic speech recognition. We define the Arabic speech recognition as a multi-agent-system where each agent has a specific goal and deals with that goal only. Once all the small tasks are accomplished the big task is too. A number of agents are required in order to recast Arabic speech recognition, namely the feature extraction agent and the pattern classification agent. These agents are detailed in this paper.

关键词： Speech recognition Feature extraction Natural languages Hidden Markov models Automatic speech recognition Intelligent agent Pattern classification linear predictive coding Intelligent sensors Standards development

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Development of a 6.6 kV - 1 MVA Transformerless Loop Balance Controller

Development of a 6.6 kV - 1 MVA Transformerless Loop Balance...

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Annual IEEE Conference on Power Electronics Specialists (PESC)

作者： Naotaka Okada Masahiro Takasaki Hiromitsu Sakai Shuji Katoh Central Research Institute of Electric Power Industry Komae Tokyo Japan Hitachi and Limited Hitachi Ibaraki Japan

To use distribution networks effectively and ensure the smooth introduction of distributed generations, a loop or mesh structure for distribution systems has been proposed using back to back type loop power flow controller (LPC) by CRIEPI. The installation space of looping equipment is restricted in power distribution systems. It is necessary to miniaturize the equipment. We developed 6.6 kV-1MVA transformerless loop balance controller (LBC) as one approach to miniaturization of LPC. A development of LBC has been entrusted by a national project: "Demonstrative Project on Power Network Technology" from the New Energy and Industrial Technology Development Organization (NEDO).

关键词： Control systems linear predictive coding Substations Electrical equipment industry Load flow Voltage control System testing Centralized control Distributed control Space technology

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Selected applications of LPLE in speech processing

Selected applications of LPLE in speech processing

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IEEE Southeastcon

作者： Norelli Schettini Ravi Sankar Department of Electrical Engineering University of South Florida Tampa FL USA

linear prediction with low-frequency emphasis (LPLE), an all-pole modeling technique which emphasizes the lower frequency range of the input signal, was described by Alku and Backstrom (Alku, 2004). The method is based on first interpreting conventional linear predictive (LP) analyses of successive prediction orders with parallel structures using the concept of symmetric linear prediction. The experiments presented in this work are aimed to show that the LPLE method is well-suited for those speech processing and enhancement applications, where low-order all-pole models with improved modeling of the lowest formants are needed. This is done by replacing the LP block with a LPLE block. Distortion measures using Itakura distance and noise cancellation using a Wiener "type" filter were explored, utilizing the LPLE coefficients instead of LP coefficients.

关键词： Speech processing predictive models Frequency Speech analysis linear predictive coding Distortion measurement Humans Auditory system Nonlinear filters Signal processing

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The application of information fusion in the real-time monitoring system

The application of information fusion in the real-time monit...

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International Conference on Information Fusion

作者： Wang Chen Miao Zhenjiang Meng Xiao Institute of Information Science Beijing Jiaotong University Beijing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9780662478300;0662478304

This paper studies the feasibility of information analysis processing technology, which fuses speech and image together in the real-time monitoring system. It emphasizes particularly on speech analysis and fuses these two technologies in terms of scoring strategy. It also makes some improvement on MFCC feature extraction and proposes a quick MFCC algorithm. The proposed algorithm can reach the requirement of real-time system in case of the high precision. To prove it, this paper compares its algorithm with LPC and FFT. The experiment indicates that the EER of LPC is 13.9% and the EER of FFT is 11.1%, but by using the Quick MFCC the EER is only 4.2%. And compared with the traditional MFCC algorithm, the quick MFCC algorithm reduces the run time greatly while maintaining recognition accuracy of the system. Finally the rate of fusion recognition is about 97.8%, which is a good result for the real-time monitoring system.

关键词： Real time systems Monitoring Mel frequency cepstral coefficient Speech analysis Information analysis Feature extraction Speaker recognition Fuses Speech processing linear predictive coding

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Evalutation of Performance of KNN, MLP and RBF Classifiers in Emotion Detection Problem

Evalutation of Performance of KNN, MLP and RBF Classifiers i...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Gokhan Polat Halis Altun Elektrik-Elektronik Mühendisi Nigde Üniversitesi Yüksek Lisans Öğrencisi Nigde Turkey Istanbul Tip Fakültesi Nöroloji Anabilim Dali Mühendislik Mimarlik Fakültesi Nigde Turkey

Emotion detection has gained increasing attention and become an active research area. The problem is solved with improved feature set with different number of feature groups, by employing different classifiers in order to achieve satisfactory recognition rate. In this study, speech related features are employed to evaluate the performance of different classifiers in emotion detection problem.

关键词： Hidden Markov models Mel frequency cepstral coefficient linear predictive coding Speech analysis Performance evaluation linear discriminant analysis

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Applying Tunstall coding in the existing SEED format for Seismographic Data

Applying Tunstall Coding in the existing SEED format for Sei...

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Data Compression Conference (DCC)

作者： Edwin S. Hong Shu-Fang Newman Computing and Sof tware Systems Institute of Technology University of Washington Tacoma USA

The Standard for the Exchange of Earthquake Data (SEED) is a commonly used file format for recording seismographic data. The data description language (DDL) is part of the specification of the SEED format; it is used to describe the way in which the data has been encoded in the SEED file. All SEED files contain a piece of DDL code that describes how to read the data contained in the file. In this project, we explore the best possible way of compressing seismographic data losslessly under the constraint that it must be describable in DDL. Since DDL is not a Turing-complete language, it is impossible to implement many standard compression algorithms with it. However, we show it is possible to implement a modified Tunstall code under DDL that produces files that are on average 14% smaller than the traditionally used Steim2 compression technique. We also compare our results with a standard linear predictive coding scheme (which is not implementable in DDL) and show that our technique is on average 18% worse on the same set of files.

关键词： Encoding Statistics Software systems Software standards Earthquakes Compression algorithms linear predictive coding Testing Decoding Data compression

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Voice Conversion Using Canonical Correlation Analysis Based on Gaussian Mixture Model

Voice Conversion Using Canonical Correlation Analysis Based ...

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： ZhiHua Jian Zhen Yang Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing China

A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the canonical correlation analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after linear prediction (LPC) analysis, the CCA method is more suitable for spectral conversion than MMSE because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both subjective and objective evaluations are conducted. The experimental results demonstrate that the proposed scheme can achieve better performance than the previous method which uses MMSE estimation criterion.

关键词： Speech analysis Loudspeakers linear predictive coding Speech synthesis Artificial neural networks Signal processing algorithms Vectors Analysis of variance Information analysis Hidden Markov models

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