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检索条件"主题词=Linear predictive coding"

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Frame level entropy based overlapped speech detection as a pre-processing stage for speaker diarization

Frame level entropy based overlapped speech detection as a p...

引用

IEEE Workshop on Machine Learning for Signal Processing

作者： Oshry Ben-Harush Hugo Guterman Itshak Lapidot Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beersheba Israel Department of Elect and Electronics Engineering Sami Shamoon College of Engineering Ashdod Israel

Speaker diarization systems attempt to assign temporal speech segments in a conversation to the appropriate speaker, and non-speech segments to non-speech. Speaker diarization systems basically provide an answer to the question "Who spoke when ?". One inherent deficiency of most current systems is their inability to handle co-channel or overlapped speech. During the past few years, several studies have attempted dealing with the problem of overlapped or co-channel speech detection and separation, however, most of the algorithms suggested perform under unique conditions, require high computational complexity and require both time and frequency domain analysis of the audio data. In this study, frame based entropy analysis of the audio data in the time domain serves as a single feature for an overlapped speech detection algorithm. Identification of overlapped speech segments is performed using Gaussian Mixture Modeling (GMM) along with well known classification algorithms applied on two speaker conversations. By employing this methodology, the proposed method eliminates the need for setting a hard threshold for each conversation or database. LDC CALLHOME American English corpus is used for evaluation of the suggested algorithm. The proposed method successfully detects 60.0% of the frames labeled as overlapped speech by the baseline (ground-truth) segmentation , while keeping a 5% false-alarm rate.

关键词： Entropy Speech analysis Automatic speech recognition Detection algorithms Speech processing Hidden Markov models Autocorrelation linear predictive coding Educational institutions Computational complexity

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A two pass classifier for utterance rejection in keyword spotting

A two pass classifier for utterance rejection in keyword spo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： R.A. Sukkar J.G. Wilpon AT and T Bell Laboratories Inc. Naperville IL USA

A classifier for utterance rejection in a hidden Markov model (HMM) based speech recognizer is presented. This classifier, termed the two-pass classifier, is a postprocessor to the HMM recognizer, and consists of a two-stage discriminant analysis. The first stage employs the generalized probabilistic descent (GPD) discriminative training framework, while the second stage performs linear discrimination combining the output of the first stage with HMM likelihood scores. In this fashion the classification power of the HMM is combined with that of the GPD stage which is specifically designed for keyword/nonkeyword classification. Experimental results show that, on two separate databases, the two-pass classifier significantly outperforms a single-pass classifier based solely on the HMM likelihood scores.< >

关键词： Hidden Markov models Cepstral analysis Monitoring Automatic speech recognition Isolation technology Speech recognition Databases Telephony Robustness linear predictive coding

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Wavelet based feature extraction for phoneme recognition

Wavelet based feature extraction for phoneme recognition

引用

International Conference on Spoken Language, ICSLP

作者： C.J. Long S. Datta Department of Electronic and Electrical Engineering Loughborough University of Technology Loughborough UK

In an effort to provide a more efficient representation of the acoustical speech signal in the pre classification stage of a speech recognition system, we consider the application of the Best-Basis Algorithm of R.R. Coifman and M.L. Wickerhauser (1992). This combines the advantages of using a smooth, compactly supported wavelet basis with an adaptive time scale analysis, dependent on the problem at hand. We start by briefly reviewing areas within speech recognition where the wavelet transform has been applied with some success. Examples include pitch detection, formant tracking, phoneme classification. Finally, our wavelet based feature extraction system is described and its performance on a simple phonetic classification problem given.

关键词： Feature extraction Multiresolution analysis Continuous wavelet transforms Discrete wavelet transforms Speech recognition Wavelet analysis Wavelet transforms Time frequency analysis Signal analysis linear predictive coding

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A new BSP excitation signal for linear prediction-based speech codecs

A new BSP excitation signal for linear prediction-based spee...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Deng Lixin Yang Zhen Zheng Baoyu Information Engineering Department Nanjing University of Posts and Telecommunications China

We present in this paper a new binomial sine pulse (BSP) excitation signal used in linear prediction-based speech codecs. The structure of the BSP excitation signal is actually a sine wave whose amplitude is modulated by a binomial signal. The binomial signal describes the various trends of excitation signals in a pitch period, and the pulsatance of the BSP excitation signal coincides with the vibration frequency of vocal folds. In experiments, processing is going on frame by frame and the same excitation signal is placed at every pitch excitation moment in a frame. Speech codecs based on this new BSP excitation have the advantages of low complexity and low delay. Experiment results prove that such a new speech codec can provide highly intelligible synthesized speech below 3 kbps.

关键词： Speech codecs Speech synthesis Speech coding Signal synthesis Signal processing Bit rate Shape linear predictive coding Filters Telecommunications

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The application of a functional perceptual model of speech to variable-rate LPC systems

The application of a functional perceptual model of speech t...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： R. Viswanathan J. Makhoul R. Wicke Bolt Beranek and Newman 슠Inc. Cambridge MA USA

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Automatic speech recognition using hidden Markov models and artificial neural networks

Automatic speech recognition using hidden Markov models and ...

引用

International Conference on Neural Networks

作者： N.M. Botros M. Siddiqi M.Z. Deiri Department of Electrical Engineering Southem Illinois University Carbondale IL USA

An algorithm is presented for isolated-word recognition, taking into consideration the duration variability of the different utterances of the same word. The algorithm is based on extracting acoustical features from the speech signal and using them as the input to multilayer perceptrons neural networks. The backpropagation algorithm is used to train the networks. The hidden Markov model (HMM) is implemented to extract temporal features (states) from the speech signal. The input vector to the network consists of 16 cepstral coefficients, two delta cepstral coefficients, and five elements to represent the state. The networks are trained to recognize the correct words and to reject the wrong words. The training set consists of ten words (digit zero to digit nine), each uttered seven times, by three different speakers. The test set consists of three utterances of each of the ten words. The authors' results show the ability to recognize all of these words.< >

关键词： Automatic speech recognition Hidden Markov models Artificial neural networks Cepstral analysis Feature extraction Band pass filters Vectors linear predictive coding Multilayer perceptrons Neural networks

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Spectral stability based event localizing temporal decomposition

Spectral stability based event localizing temporal decomposi...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： A.C.R. Nandasena M. Akagi Graduate School of Information Science Japan Advanced Institute of Science and Technology Japan

A new approach to temporal decomposition (TD) of speech, called "spectral stability based event localizing temporal decomposition", abbreviated S/sup 2/ BEL-TD, is presented. The original method of TD proposed by Atal (1983) is known to have the drawbacks of high computational cost, and the instability of the number and locations of events. In S/sup 2/ BEL-TD, the event localization is performed based on a maximum spectral stability criterion. This overcomes the instability problem of events of the Atal's method. Also, S/sup 2/ BEL-TD avoids the use of the computationally costly singular value decomposition routine used in the Atal's method, thus resulting in a computationally simpler algorithm of TD. Simulation results show that an average spectral distortion of about 1.5 dB can be achieved with LSF as the spectral parameter. Also, we have shown that the temporal pattern of the speech excitation parameters can also be well described using the S/sup 2/ BEL-TD technique.

关键词： Speech Singular value decomposition Information science Costs Computational modeling linear predictive coding Solids Stability criteria

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Classification Using the Local Probabilistic Centers of k-Nearest Neighbors

Classification Using the Local Probabilistic Centers of k-Ne...

引用

International Conference on Pattern Recognition

作者： Bo Yu Li Yun Wen Chen Department of Computer Science and Engineering Fudan University Shanghai China

ISBN: (纸本)0769525210

In high dimensional feature space with finite samples, severe bias can be introduced in the nearest neighbor algorithm. In this paper, we propose a new classification method, which performs classification task based on local probability center of each class. Moreover, this prototype-based method classifies the query sample by using two measures, one is the distance between query and local probability centers, the other is the posterior probability of query. Although both measures are effect, the experiments show the second one is the better. The investigation results prove that this method improves the classification performance of nearest neighbor algorithm substantially

关键词： Nearest neighbor searches Neural networks Support vector machines linear predictive coding Prototypes Computational efficiency Computer science Pattern classification Training data Support vector machine classification

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Automatic classification of singing voice quality

Automatic classification of singing voice quality

引用

International Conference on Intelligent Systems Design and Applications (ISDA)

作者： B. Kostek P. Zwan Multimedia Systems Department Gdańsk University of Technology Poland

In the paper problems related to the classification of singing voice quality are presented. For this purpose a database consisting of singers' sample recordings is constructed and parameters are extracted from recorded voice of trained and untrained singers. The parameterization process is based on both voice source and formant analysis of a singing voice. These parameters are explained as to their physical interpretation and analyzed statistically in order to diminish their number. The statistical analysis is based on the Fisher statistic. In such a way a feature vector of a singing voice is formed. Decision systems based on neutral networks and rough sets are utilized in the context of the voice type and voice quality classification. Results obtained in the automatic classification performed by both decision systems are compared. A possibility to classify automatically type/quality of voice is judged. The methodology proposed provides means for discerning trained and untrained singers.

关键词： Frequency Statistical analysis Speech analysis Rough sets Biomechanics Intelligent systems Signal analysis linear predictive coding Paper technology Multimedia systems

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Backward adaptive prediction: high-order predictors and formant-pitch configurations

Backward adaptive prediction: high-order predictors and form...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： M. Foodeei P. Kabal Electrical Engineering McGill University Montreal QUE Canada INRS-Télécommunications Université du Quàbec Verdun QUE Canada

An analysis and simulation results are presented comparing the performance of several types of high-order backward adaptive predictors with orders up to 100. Issues in high-order linear predictive coding (LPC) analysis, such as analysis methods, windowing, ill-conditioning, quantization noise effects, and computational complexity, are studied. The performance of the various analysis methods is compared with the conventional sequential formant-pitch predictor. The auto-correlation method (50th order) shows performance advantages over the sequential formant-pitch configurations. Several new backward high-order methods using covariance analysis and a lattice formulation show much better prediction gains than the auto-correlation method.< >

关键词： Redundancy Speech Performance analysis Quantization Adaptive filters Shape control Computational modeling Analytical models predictive models linear predictive coding

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