523 research outputs found

    Predicción lineal de la parte causal de la autocorrelación para la identificación del locutor en ambientes ruidosos

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    Recently, a new parametrization technique based on the AR modelling of the one-sided autocorrelation sequence (OSALPC) has shown to be attractive for speech recognition because of its simplicity and its high recognition perfomance in noisy conditions. In this paper, that new parametrization technique is proposed to speaker identification in noisy enviroment. Experimental results obtained with a new speaker identification system based on the statistics of the cepstrals vectors show that OSALPC also achieves much better results than standard parametrization techniques.Peer ReviewedPostprint (published version

    AR modeling of the speech autocorrelation to improve noisy speech recognition

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    Speech recognition in noisy environments remains an unsolved problem even in the case of isolated word recognition with small vocabularies. Recently, several techniques have been proposed to alleviate this problem. Concretely, two closely related parameterization techniques based on an AR modelling in the autocorrelation domain called SMC [1] and OSALPC [2] have shown good results using speech contaminated by additive white noise. The aim of this paper is twofold: to compare several techniques based on an AR modelling in the autocorrelation domain, including SMC and OSALPC, and to find the optimum model order and cepstral liftering for noisy conditions.Peer ReviewedPostprint (published version

    Some fast higher order ar estimation techniques applied to parametric wiener filtering

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    Some Speech Enhancement algorithms based on the iterative Wiener filtering Method due to L1m-Oppenheim [2] are presented. In the original Lim-Oppenheim algorithm, speech AR estimation is carried out using classic second-order analysis, but our algorithms consider a more robust AR modelling. Two different strategies of speech AR estimation are presented and both estimators are trying to see as less amount of noise as possible. First one uses a previous One-Sided Autocorrelation computation, that is a pole-preserving function, and the actual SNR m the second-order LPC analysis is increased. Second one combines advantages of Higher-Order Statistics [1] with a linear combination of AR coefficients, belonging to two consecutive overlapped frames, to assess a less disturbed speech estimation.Peer ReviewedPostprint (published version

    A robust feature extraction for automatic speech recognition in noisy environments

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    This paper presents a method for extraction of speech robust features when the external noise is additive and has white noise characteristics. The process consists of a short time power normalisation which goal is to preserve as much as possible, the speech features against noise. The proposed normalisation will be optimal if the corrupted process has, as the noise process white noise characteristics. With optimal normalisation we can mean that the corrupting noise does not change at all the means of the observed vectors of the corrupted process. As most of the speech energy is contained in a relatively small frequency band being most of the band composed by noise or noise-like power, this normalisation process can still capture most of the noise distortions. For Signal to Noise Ratio greater than 5 dB the results show that for stationary white noise, the normalisation process where the noise characteristics are ignored at the test phase, outperforms the conventional Markov models composition where the noise is known. If the noise is known, a reasonable approximation of the inverted system can be easily obtained performing noise compensation still increasing the recogniser performance

    New Features Using Robust MVDR Spectrum of Filtered Autocorrelation Sequence for Robust Speech Recognition

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    This paper presents a novel noise-robust feature extraction method for speech recognition using the robust perceptual minimum variance distortionless response (MVDR) spectrum of temporally filtered autocorrelation sequence. The perceptual MVDR spectrum of the filtered short-time autocorrelation sequence can reduce the effects of residue of the nonstationary additive noise which remains after filtering the autocorrelation. To achieve a more robust front-end, we also modify the robust distortionless constraint of the MVDR spectral estimation method via revised weighting of the subband power spectrum values based on the sub-band signal to noise ratios (SNRs), which adjusts it to the new proposed approach. This new function allows the components of the input signal at the frequencies least affected by noise to pass with larger weights and attenuates more effectively the noisy and undesired components. This modification results in reduction of the noise residuals of the estimated spectrum from the filtered autocorrelation sequence, thereby leading to a more robust algorithm. Our proposed method, when evaluated on Aurora 2 task for recognition purposes, outperformed all Mel frequency cepstral coefficients (MFCC) as the baseline, relative autocorrelation sequence MFCC (RAS-MFCC), and the MVDR-based features in several different noisy conditions

    Investigation of the impact of high frequency transmitted speech on speaker recognition

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    Thesis (MScEng)--Stellenbosch University, 2002.Some digitised pages may appear illegible due to the condition of the original hard copy.ENGLISH ABSTRACT: Speaker recognition systems have evolved to a point where near perfect performance can be obtained under ideal conditions, even if the system must distinguish between a large number of speakers. Under adverse conditions, such as when high noise levels are present or when the transmission channel deforms the speech, the performance is often less than satisfying. This project investigated the performance of a popular speaker recognition system, that use Gaussian mixture models, on speech transmitted over a high frequency channel. Initial experiments demonstrated very unsatisfactory results for the base line system. We investigated a number of robust techniques. We implemented and applied some of them in an attempt to improve the performance of the speaker recognition systems. The techniques we tested showed only slight improvements. We also investigates the effects of a high frequency channel and single sideband modulation on the speech features of speech processing systems. The effects that can deform the features, and therefore reduce the performance of speech systems, were identified. One of the effects that can greatly affect the performance of a speech processing system is noise. We investigated some speech enhancement techniques and as a result we developed a new statistical based speech enhancement technique that employs hidden Markov models to represent the clean speech process.AFRIKAANSE OPSOMMING: Sprekerherkenning-stelsels het 'n punt bereik waar nabyaan perfekte resultate verwag kan word onder ideale kondisies, selfs al moet die stelsel tussen 'n groot aantal sprekers onderskei. Wanneer nie-ideale kondisies, soos byvoorbeeld hoë ruisvlakke of 'n transmissie kanaal wat die spraak vervorm, teenwoordig is, is die resultate gewoonlik nie bevredigend nie. Die projek ondersoek die werksverrigting van 'n gewilde sprekerherkenning-stelsel, wat gebruik maak van Gaussiese mengselmodelle, op spraak wat oor 'n hoë frekwensie transmissie kanaal gestuur is. Aanvanklike eksperimente wat gebruik maak van 'n basiese stelsel het nie goeie resultate opgelewer nie. Ons het 'n aantal robuuste tegnieke ondersoek en 'n paar van hulle geïmplementeer en getoets in 'n poging om die resultate van die sprekerherkenning-stelsel te verbeter. Die tegnieke wat ons getoets het, het net geringe verbetering getoon. Die studie het ook die effekte wat die hoë-frekwensie kanaal en enkel-syband modulasie op spraak kenmerkvektore, ondersoek. Die effekte wat die spraak kenmerkvektore kan vervorm en dus die werkverrigting van spraak stelsels kan verlaag, is geïdentifiseer. Een van die effekte wat 'n groot invloed op die werkverrigting van spraakstelsels het, is ruis. Ons het spraak verbeterings metodes ondersoek en dit het gelei tot die ontwikkeling van 'n statisties gebaseerde spraak verbeteringstegniek wat gebruik maak van verskuilde Markov modelle om die skoon spraakproses voor te stel

    Wavelet-based techniques for speech recognition

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    In this thesis, new wavelet-based techniques have been developed for the extraction of features from speech signals for the purpose of automatic speech recognition (ASR). One of the advantages of the wavelet transform over the short time Fourier transform (STFT) is its capability to process non-stationary signals. Since speech signals are not strictly stationary the wavelet transform is a better choice for time-frequency transformation of these signals. In addition it has compactly supported basis functions, thereby reducing the amount of computation as opposed to STFT where an overlapping window is needed. [Continues.

    A Method for Compressing Parameters in Bayesian Models with Application to Logistic Sequence Prediction Models

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    Bayesian classification and regression with high order interactions is largely infeasible because Markov chain Monte Carlo (MCMC) would need to be applied with a great many parameters, whose number increases rapidly with the order. In this paper we show how to make it feasible by effectively reducing the number of parameters, exploiting the fact that many interactions have the same values for all training cases. Our method uses a single ``compressed'' parameter to represent the sum of all parameters associated with a set of patterns that have the same value for all training cases. Using symmetric stable distributions as the priors of the original parameters, we can easily find the priors of these compressed parameters. We therefore need to deal only with a much smaller number of compressed parameters when training the model with MCMC. The number of compressed parameters may have converged before considering the highest possible order. After training the model, we can split these compressed parameters into the original ones as needed to make predictions for test cases. We show in detail how to compress parameters for logistic sequence prediction models. Experiments on both simulated and real data demonstrate that a huge number of parameters can indeed be reduced by our compression method.Comment: 29 page
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