Wavelet methods in speech recognition

In this thesis, novel wavelet techniques are developed to improve parametrization of speech signals prior to classification. It is shown that non-linear operations carried out in the wavelet domain improve the performance of a speech classifier and consistently outperform classical Fourier methods. This is because of the localised nature of the wavelet, which captures correspondingly well-localised time-frequency features within the speech signal. Furthermore, by taking advantage of the approximation ability of wavelets, efficient representation of the non-stationarity inherent in speech can be achieved in a relatively small number of expansion coefficients. This is an attractive option when faced with the so-called 'Curse of Dimensionality' problem of multivariate classifiers such as Linear Discriminant Analysis (LDA) or Artificial Neural Networks (ANNs). Conventional time-frequency analysis methods such as the Discrete Fourier Transform either miss irregular signal structures and transients due to spectral smearing or require a large number of coefficients to represent such characteristics efficiently. Wavelet theory offers an alternative insight in the representation of these types of signals. As an extension to the standard wavelet transform, adaptive libraries of wavelet and cosine packets are introduced which increase the flexibility of the transform. This approach is observed to be yet more suitable for the highly variable nature of speech signals in that it results in a time-frequency sampled grid that is well adapted to irregularities and transients. They result in a corresponding reduction in the misclassification rate of the recognition system. However, this is necessarily at the expense of added computing time. Finally, a framework based on adaptive time-frequency libraries is developed which invokes the final classifier to choose the nature of the resolution for a given classification problem. The classifier then performs dimensionaIity reduction on the transformed signal by choosing the top few features based on their discriminant power. This approach is compared and contrasted to an existing discriminant wavelet feature extractor. The overall conclusions of the thesis are that wavelets and their relatives are capable of extracting useful features for speech classification problems. The use of adaptive wavelet transforms provides the flexibility within which powerful feature extractors can be designed for these types of application

Wavelet-based techniques for speech recognition

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 target guided subband filter for acoustic event detection in noisy environments using wavelet packets

This paper deals with acoustic event detection (AED), such as screams, gunshots, and explosions, in noisy environments. The main aim is to improve the detection performance under adverse conditions with a very low signal-to-noise ratio (SNR). A novel filtering method combined with an energy detector is presented. The wavelet packet transform (WPT) is first used for time-frequency representation of the acoustic signals. The proposed filter in the wavelet packet domain then uses a priori knowledge of the target event and an estimate of noise features to selectively suppress the background noise. It is in fact a content-aware band-pass filter which can automatically pass the frequency bands that are more significant in the target than in the noise. Theoretical analysis shows that the proposed filtering method is capable of enhancing the target content while suppressing the background noise for signals with a low SNR. A condition to increase the probability of correct detection is also obtained. Experiments have been carried out on a large dataset of acoustic events that are contaminated by different types of environmental noise and white noise with varying SNRs. Results show that the proposed method is more robust and better adapted to noise than ordinary energy detectors, and it can work even with an SNR as low as -15 dB. A practical system for real time processing and multi-target detection is also proposed in this work

Off-line handwritten signature recognition by wavelet entropy and neural network

Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%

Generative Adversarial Network with Convolutional Wavelet Packet Transforms for Automated Speaker Recognition and Classification

Speech is an effective mode of communication that always conveys abundant and pertinent information, such as the gender, accent, and other distinguishing characteristics of the speaker. These distinctive characteristics allow researchers to identify human voices using artificial intelligence (AI) techniques, which are useful for forensic voice verification, security and surveillance, electronic voice eavesdropping, mobile banking, and mobile purchasing. Deep learning (DL) and other advances in hardware have piqued the interest of researchers studying automatic speaker identification (SI). In recent years, Generative Adversarial Networks (GANs) have demonstrated exceptional ability in producing synthetic data and improving the performance of several machine learning tasks. The capacity of Convolutional Wavelet Packet Transform (CWPT) and Generative Adversarial Networks are combined in this paper to propose a novel way of enhancing the accuracy and robustness of Speaker Recognition and Classification systems. Audio signals are dissected using the Convolutional Wavelet Packet Transform into a multi-resolution, time-frequency representation that faithfully preserves local and global characteristics. The improved audio features better precisely describe speech traits and handle pitch, tone, and pronunciation variations that are frequent in speaker recognition tasks. Using GANs to create synthetic speech samples, our suggested method GAN-CWPT enriches the training data and broadens the dataset's diversity. The generator and discriminator components of the GAN architecture have been tweaked to produce realistic speech samples with attributes quite similar to genuine speaker utterances. The new dataset enhances the Speaker Recognition and Classification system's robustness and generalization, even in environments with little training data. We conduct extensive tests on standard speaker recognition datasets to determine how well our method works. The findings demonstrate that, compared to conventional methods, the GAN-CWPTs combination significantly improves speaker recognition, classification accuracy, and efficiency. Additionally, the suggested model GAN-CWPT exhibits stronger generalization on unknown speakers and excels even with loud and poor audio inputs

WAVELET ANALYSIS CONSIDERATIONS FOR EXPERIMENTAL NONSTATIONARY FLOW PHENOMENA

In this work, wavelet transforms are the analysis tools for studying transient and discontinuous phenomena associated to turbulent flows. The application in quest results from velocity measurements with hot wire anemometry in the transient wake considering a circular cylinder in an aerodynamic channel. Continuous and discrete wavelet transforms are applied and compared with the corresponding results given by the Fourier transform. For the continuous wavelet transform, the Morlet function was adopted as transform basis, and for the discrete case, the Daubechies orthonormal wavelet with 20 null moments. Results using the discrete wavelet packet transform are also presented and compared. A wake past a cylinder was analytically simulated and compared with the actual one, both in transient flow. The ability of the wavelet transforms in the analysis of unsteady phenomena and the potential of the wavelet approach as a complementary tool to the Fourier spectrum for the analysis of stationary phenomena is presented and discussed

Identification of Transient Speech Using Wavelet Transforms

It is generally believed that abrupt stimulus changes, which in speech may be time-varying frequency edges associated with consonants, transitions between consonants and vowels and transitions within vowels are critical to the perception of speech by humans and for speech recognition by machines. Noise affects speech transitions more than it affects quasi-steady-state speech. I believe that identifying and selectively amplifying speech transitions may enhance the intelligibility of speech in noisy conditions. The purpose of this study is to evaluate the use of wavelet transforms to identify speech transitions. Using wavelet transforms may be computationally efficient and allow for real-time applications. The discrete wavelet transform (DWT), stationary wavelet transform (SWT) and wavelet packets (WP) are evaluated. Wavelet analysis is combined with variable frame rate processing to improve the identification process. Variable frame rate can identify time segments when speech feature vectors are changing rapidly and when they are relatively stationary. Energy profiles for words, which show the energy in each node of a speech signal decomposed using wavelets, are used to identify nodes that include predominately transient information and nodes that include predominately quasi-steady-state information, and these are used to synthesize transient and quasi-steady-state speech components. These speech components are estimates of the tonal and nontonal speech components, which Yoo et al identified using time-varying band-pass filters. Comparison of spectra, a listening test and mean-squared-errors between the transient components synthesized using wavelets and Yoo's nontonal components indicated that wavelet packets identified the best estimates of Yoo's components. An algorithm that incorporates variable frame rate analysis into wavelet packet analysis is proposed. The development of this algorithm involves the processes of choosing a wavelet function and a decomposition level to be used. The algorithm itself has 4 steps: wavelet packet decomposition; classification of terminal nodes; incorporation of variable frame rate processing; synthesis of speech components. Combining wavelet analysis with variable frame rate analysis provides the best estimates of Yoo's speech components