Automatic speech/speaker recognition in noisy environments using wavelet transform

Alkhaldi, Weaam; Fakhr, W.; Hamdy, N.

doi:10.1109/mwscas.2002.1187258

Cited by 5 publications

(4 citation statements)

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“…These extracted features must be able to discriminate between classes while being robust to any external conditions, such as noise. Therefore, the performance of the ASR system is highly dependent on the feature extraction method chosen, since the classification stage will have to classify efficiently the input speech signal according to these extracted features [15][16][17]. Over the past few years various feature extraction methods have been proposed, namely the MFCCs, the discrete wavelet transforms (DWTs) and the linear predictive coding (LPC) [1,5].…”

Section: Automatic Speech Recognition Systemsmentioning

confidence: 99%

“…As already mentioned earlier, MFCC are not robust with respect to noise-corrupted speech signals. On the other hand, DWT were successfully used for de-noising tasks because of their ability in providing localised time and frequency information [17,31,45]. Hence, if only a part of the speech signal's frequency band is corrupted by noise, not all DWT coefficients are altered.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…Under this condition, the resultant decision surface is defined as the optimal hyperplane. The decision surface can be defined as shown in (17), where w is the weight vector, b is the bias value and φ(x i ) is the kernel function. Different kernel functions are used to map the input feature space to a higher-dimensional feature space, where the different classes are assumed to be linearly separable.…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…Hence, a narrow window is used for high frequencies and a wide window is used at low frequencies [34]. For this reason, the DWT provides an adequate model for the human auditory system, since a speech signal is analysed at decreasing frequency resolution for increasing frequencies [17].…”

Section: Automatic Speech Recognition Systemsmentioning

confidence: 99%

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Comparative study of automatic speech recognition techniques

Cutajar

Gatt

Grech

et al. 2013

IET signal process.

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Over the past decades, extensive research has been carried out on various possible implementations of automatic speech recognition (ASR) systems. The most renowned algorithms in the field of ASR are the mel-frequency cepstral coefficients and the hidden Markov models. However, there are also other methods, such as wavelet-based transforms, artificial neural networks and support vector machines, which are becoming more popular. This review article presents a comparative study on different approaches that were proposed for the task of ASR, and which are widely used nowadays. † training time increases linearly with increase in vocabulary size [42] † quantisation error in the discrete representation of speech signals [42] † temporal information is ignored [42] PCA † reduction in the feature vector's size, while retaining much of the significant information [131] † robust [59, 60] † computationally expensive for high-dimensional data [8] LDA † maximises the distance between classes, but minimises the within class distance [132] † robust [133] † sample distribution is assumed a priori to be Gaussian [63] † class samples are assumed to have equal variance [63] Classification technique Advantages Disadvantages HMM † able to model time distribution of speech signals [103] † simple to adapt [68] † capable to model a sequence of discrete or continuous symbols [13] † inputs can be of variable length [40] † based on the assumption that the probability of being in a particular state is dependent only on its preceding state, ignoring any long-term dependencies [82] † emission probabilities are arbitrarily chosen; hence, these might not even represent properly the output probabilities of the corresponding state [82] ANN (in general) † good classifiers [16, 45] † highly adequate for pattern recognition applications [16, 45] † self-organising [16, 45] † self-learning [16, 45] † self-adaptive in new environments [16, 45] † robust [7] † based on ERM; hence, prone to over training a local minima problems [45, 103] MLP † good discriminating ability [2] † unable to model time distribution of speech signals [2] † inputs have to be of fixed length [2] † able to deal with small vocabularies only [2] SOM † no a priori information is required for training a SOM [134] † can easily adapt if a new sample is presented to it [134] † capable of parallel computation [134] † SOM algorithm is not well defined mathematically; hence, values for the network parameters need to be found by trial-and-error [134] † ordered mapping obtained after the training phase may be lost when applied in real environments due to frequent adaptations [134] RBF † simple to implement [135] † Good discriminating ability [135] † robust [135] † online learning ability [135] † shift invariant in time [91] RNN † able to model time distribution of speech signals thanks to the feedback connections [95, 103] † complex training algorithm [94] † training algorithm is highly sensitive to any changes [94] FNN † does not need large amount of samples during the learning process [99] † ...

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Section: Automatic Speech Recognition Systemsmentioning

confidence: 99%

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Section: Support Vector Machinesmentioning

confidence: 99%

Section: Automatic Speech Recognition Systemsmentioning

confidence: 99%

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