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A VLSI chip for isolated speech recognition system

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1996年第3期42卷 458-467页

作者： Kim, SN Hwang, IC Kim, YW Kim, SW Department of Electronics Engineering Korea University Seoul South Korea

A VLSI processor is designed for the small-scale isolated speech recognition applications. It is a dedicated processor which detects endpoint, extracts LPC(linear predictive Coefficient) cepstral coefficients from the speech signal, and computes the spectral distances using a dynamic time warping(DTW) technique. The designed chip can recognize 1000 isolated words per second with an average recognition accuracy of 90.3%. It is designed in a 0.8 mu m CMOS technology, includes 66,760 gates, and runs with a 10MHz clock.

关键词： Very large scale integration Speech recognition CMOS technology Process design linear predictive coding Cepstral analysis Speech processing Signal processing Isolation technology Clocks

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Digital underwater acoustic voice communications

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IEEE JOURNAL OF OCEANIC ENGINEERING 1996年第2期21卷 181-192页

作者： Woodward, B Sari, H Department of Electronic and Electrical Engineering Loughborough University of Technology Leicestershire UK

This paper describes the design of an underwater acoustic diver communication system controlled by a digital signal processor. The speech signal transmission rate is compressed by using linear predictive coding (LPC) and the extracted parameters are transmitted through the water to a synchronized receiver by employing digital pulse position modulation (DPPM). The pulse position in each time frame is estimated by an energy detection and decision algorithm which enables the received LPC parameters to be recovered and used to synthesize the speech signal.

关键词： Underwater acoustics Underwater communication linear predictive coding Acoustic pulses Pulse modulation Speech synthesis Signal design Digital control Communication system control Control systems

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Lossy pole-zero modeling for speech signals

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1996年第2期4卷 81-88页

作者： Lim, IT Lee, BG LG Electronics Research Center Seoul South Korea Department of Electronics Engineering Seoul National University Seoul South Korea

This paper presents an acoustic model-based lossy pole-zero modeling for speech signals, which overcomes the limitation in the existing lossless pole-zero model that forced the numerator part of the pole-zero transfer function to be symmetric, We derive the lossy pole-zero model and its transfer function by employing the wave digital filter (WDF) adaptor formulas and by converting the fixed termination value -1 to a loss factor mu(0)(c) is an element of (-1, 1). Then we discuss how to determine the reflections coefficients of the lossy pole-zero model, For this we first employ a well-performing ARMA modeling algorithm for a pole-zero type estimation of the given speech signal and then fit the transfer function of the lossy pole-zero model to that of the ARMA model under Euclidean cost function. This procedure is demonstrated by an example using the Steiglitz-McBride ARMA estimation method with a synthetic speech signal. The lossy pole-zero modeling yields a new filter structure-namely, three-branch lattice structure-which consists of three lattice branches with the third branch terminated by the loss factor mu(0)(c) is an element of [-1, 1] and with the three branches connected by a three-port wave adaptor characterized by the area ratio sigma is an element of [0, 1]. The three-branch lattice structure is a general filter structure which becomes tbe lossless pole-zero structure when mu(0)(c) = -1 and becomes the existing all-pole lattice structure when sigma = 0.

关键词： Transfer functions Lattices linear predictive coding Digital filters Termination of employment Reflection Cost function Speech analysis Speech synthesis

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Classification of cardiac arrhythmias using fuzzy ARTMAP

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1996年第4期43卷 425-430页

作者： Ham, FM Han, S Florida Institute of Technology Electrical Engineering West University Blvd Melbourne FL USA Electrical Engineering Florida Institute of Technology Melbourne FL USA

We have investigated the QRS complex, extracted from electrocardiogram (EGG) data, using fuzzy adaptive resonance theory mapping (ARTMAP) to classify cardiac arrhythmias. Two different conditions have been analyzed: normal and abnormal premature ventricular contraction (PVC), Based on MIT/BIH database annotations, cardiac beats for normal and abnormal QRS complexes were extracted from this database, scaled, and Hamming windowed, after bandpass filtering, to yield a sequence of 100 samples for each QRS segment, From each of these sequences, two linear predictive coding (LPC) coefficients were generated using Burg's maximum entropy method, The two LPC coefficients, along with the mean-square value of the QRS complex segment, were utilized as features for each condition to train and test a fuzzy ARTMAP neural network for classification of normal and abnormal PVC conditions, The test results show that the fuzzy ARTMAP neural network can classify cardiac arrhythmias with greater than 99% specificity and 97% sensitivity.

关键词： linear predictive coding Data mining Databases Testing Fuzzy neural networks Neural networks Resonance Heart rate variability Band pass filters Filtering

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On improving performance of analysis by synthesis speech coders

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1996年第3期4卷 243-247页

作者： Cucchi, S Fratti, M Ronchi, M Alcatel Telettra Inc. Milan Italy

An algorithm for improving the performance of analysis-by-synthesis (A-by-S) speech coders is presented. It is shown that proper modifications of the A-by-S strategies commonly used to determine the excitation parameters can be devised. In particular, a redefinition of the classical objective function based on the weighted mean squared error criterion may allow a better tracking of the speech signal transients as well as a better alignement of the synthetic pitch pulses. The implementation details of the algorithm are discussed and experimental results are presented.

关键词： Speech analysis Speech synthesis Performance analysis linear predictive coding Bit rate Filters Algorithm design and analysis Signal synthesis Convergence Degradation

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Robust LPC spectrum quantization - Vector quantization by a linear mapping of a block code

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1996年第4期4卷 266-280页

作者： Hagen, R CHALMERS UNIV TECHNOL DEPT INFORMAT THEORY S-41296 GOTHENBURG SWEDEN

In this paper, we address the use of a vector quantizer (VQ) defined as a linear mapping of a block code (LMBC-VQ) in robust LPC spectrum quantization. Split VQ and multlstage VQ of LSF's are considered, We demonstrate several properties for the LMBC-VQ including design methods and channel robustness theory. The LMBC-VQ codebooks are, guided by the robustness theory, directly designed with inherent robustness against channel errors. We demonstrate results where the resulting LMBC-VQ's outperform conventional codebooks designed with the generalized Lloyd algorithm in the presence of channel errors even when postprocessing index assignment is applied to the latter. Listening tests confirmed that the increased robustness is significant for the perceptual quality. We show that short block codes can be used to build the codebooks with no or small losses in quantization performance. This is advantageous for channel error robustness, lower memory requirement, and lower complexity. We also describe the structure of split VQ and multistage VQ using the LMBC framework. This allows us to assess the structural constraints and to generalize these VQ schemes, resulting in improved performance.

关键词： Robustness linear predictive coding Block codes Filters Speech coding Speech synthesis Speech analysis Vector quantization Design methodology Algorithm design and analysis

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VLSI arrays for speech processing with linear predictive coding

VLSI arrays for speech processing with linear predictive cod...

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International Conference on Pattern Recognition

作者： Y.Y. Tang Tao Li C.Y. Suen Institute of Electronic Information Engineering Chongqing University Chongqing China Department of Computer Science Concordia University Montreal West QUE Canada Centre for Pattern Recognition and Machine Intelligence

The covariance analysis of linear predictive coding has wide applications, especially in speech recognition and speech signal processing. Real-time applications demand very high processing speed for linear predictive coding analysis. VLSI technology which possesses properties of low-cost, high-speed and massive computing capabilities is a suitable candidate. In this paper, systolic array processors for the covariance analysis of linear predictive coding are developed. The covariance analysis of linear predictive coding contains a large set of irregular and nested recurrence equations. Systolizing the algorithm is a difficult task for such a complex problem, Existing methods of systematic design for systolic arrays are not much helpful to this problem. To overcome it, a break-combination method is presented in this paper. In this manner, the task is first decomposed and then mapped onto several interconnected systolic arrays. The resulting systolic arrays of the sub problems are then combined to form a complete solution.

关键词： Very large scale integration Speech processing linear predictive coding Systolic arrays Speech analysis Signal analysis Speech recognition Speech coding Array signal processing Difference equations

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Projection-based group delay scheme for speech recognition

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1996年第2期4卷 138-140页

作者： Tung, SL Lei, IS Juang, YT Department of Electrical Engineering National Central University Chungli Taiwan National Central University Chung-Li TW Dept. of Electr. Eng. Nat. Central Univ. Chung-Li Taiwan

This paper deals with speech recognition in a noisy environment. Some robust properties of the group delay spectrum are derived, and a projection-based group delay scheme is proposed for noisy speech recognition. Experiments show that the proposed new scheme obtains better recognition results, especially under low signal-to-noise ratio (SNR) conditions.

关键词： Delay Speech recognition Speech analysis Distortion measurement Additive noise Working environment noise Cepstrum Noise reduction linear predictive coding Signal to noise ratio

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Segmentation based linear predictive coding of multispectral images

Segmentation based linear predictive coding of multispectral...

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IEEE International Conference on Image Processing

作者： Jian-Hong Hu Yao Wang P. Cahill Departments of Electrical Engineering Polytechnic University Brooklyn NY USA

This paper presents a segmentation based linear predictive coding (SLPC) method for multispectral images. Given a set of multispectral images, the SLPC method first segments it into statistically distinct regions. It then finds a suitable linear prediction model for each region. Finally, it quantizes the prediction error in each class using a vector quantizer. The original image set is described by the segmentation map, the model parameters for each class, and the quantized prediction errors. The SLPC method can produce very high compression gains, because the specification of the segmentation map and model parameters requires significantly fewer bits than that for the original intensity values. This method has been applied to magnetic resonance head images with three spectral bands (one T1 weighted and two T2 weighted, 256/spl times/256/spl times/12 bits/image). Images compressed by a factor of more than 22 have been regarded as indistinguishable from the originals, by several radiologists.< >

关键词： Image segmentation linear predictive coding Multispectral imaging Autoregressive processes Image coding predictive models Biomedical imaging Head Radiology Educational institutions

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A statistical pattern recognition approach to robust recursive identification of nonstationary AR model of speech production system

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1996年第6期4卷 456-460页

作者： Markovic, MZ Kovacevic, BD Milosavljevic, MM Institute of Applied Mathematics and Electronics Belgrade Serbia Faculty of Electrical Engineering University of Belgrade Belgrade Serbia

We propose a new robust recursive procedure based an WRLS algorithm with VFF and frame-based quadratic classifier for identification of nonstationary AR model of speech. Two versions of the frame-based quadratic classifier design procedure are elaborated upon. Experimental results are obtained in analyzing speech signal on voiced and mixed excitation frames.

关键词： Pattern recognition Robustness linear predictive coding Production systems Speech enhancement Signal analysis Speech analysis Speech coding Least squares methods Gaussian noise

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