Robust ASR using Support Vector Machines

The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units. In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM–SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in Shimodaira et al. [Shimodaira, H., Noma, K., Nakai, M., Sagayama, S., 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In: Proc. Eurospeech, Aalborg, Denmark, pp. 1841–1844] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.Publicad

A frequency-selective feedback model of auditory efferent suppression and its implications for the recognition of speech in noise

The potential contribution of the peripheral auditory efferent system to our understanding of speech in a background of competing noise was studied using a computer model of the auditory periphery and assessed using an automatic speech recognition system. A previous study had shown that a fixed efferent attenuation applied to all channels of a multi-channel model could improve the recognition of connected digit triplets in noise [G. J. Brown, R. T. Ferry, and R. Meddis, J. Acoust. Soc. Am. 127, 943?954 (2010)]. In the current study an anatomically justified feedback loop was used to automatically regulate separate attenuation values for each auditory channel. This arrangement resulted in a further enhancement of speech recognition over fixed-attenuation conditions. Comparisons between multi-talker babble and pink noise interference conditions suggest that the benefit originates from the model?s ability to modify the amount of suppression in each channel separately according to the spectral shape of the interfering sounds

Speaker Identification and Spoken word Recognition in Noisy Environment using Different Techniques

In this work, an attempt is made to design ASR systems through software/computer programs which would perform Speaker Identification, Spoken word recognition and combination of both speaker identification and Spoken word recognition in general noisy environment. Automatic Speech Recognition system is designed for Limited vocabulary of Telugu language words/control commands. The experiments are conducted to find the better combination of feature extraction technique and classifier model that will perform well in general noisy environment (Home/Office environment where noise is around 15-35 dB). A recently proposed features extraction technique Gammatone frequency coefficients which is reported as the best fit to the human auditory system is chosen for the experiments along with the more common feature extraction techniques MFCC and PLP as part of Front end process (i.e. speech features extraction). Two different Artificial Neural Network classifiers Learning Vector Quantization (LVQ) neural networks and Radial Basis Function (RBF) neural networks along with Hidden Markov Models (HMMs) are chosen for the experiments as part of Back end process (i.e. training/modeling the ASRs). The performance of different ASR systems that are designed by utilizing the 9 different combinations (3 feature extraction techniques and 3 classifier models) are analyzed in terms of spoken word recognition and speaker identification accuracy success rate, design time of ASRs, and recognition / identification response time .The testing speech samples are recorded in general noisy conditions i.e.in the existence of air conditioning noise, fan noise, computer key board noise and far away cross talk noise. ASR systems designed and analyzed programmatically in MATLAB 2013(a) Environment

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Identity Verification with Speech Recognition : A Study

Since verification and authentication based on speech recognition are important develop-ments in the field of information security, this study revolves around the topic of speech-based Identity verification. The goal was to create a speech recognition system that can be embedded to an existing user authentication system. For this purpose, the study relied on a free speech modelling tool and modelled a limited dictionary for speech recognition. Speech samples were collected for training and testing purposes. The training sample was collected from three respondents and the testing data from seven, including those from previous three respondents. A freely available tool, HTK Toolkit, was used to train the model with speech samples recorded from respondents. The results showed that the accuracy of the model was dependent on whether the training da-taset included the users’ speech or not. The study supports the significance of real use of the model. However, the scope of the study is not large enough for authentication and verification system

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Towards A Robust Arabic Speech Recognition System Based On Reservoir Computing

In this thesis we investigate the potential of developing a speech recognition system based on a recently introduced artificial neural network (ANN) technique, namely Reservoir Computing (RC). This technique has, in theory, a higher capability for modelling dynamic behaviour compared to feed-forward ANNs due to the recurrent connections between the nodes in the reservoir layer, which serves as a memory. We conduct this study on the Arabic language, (one of the most spoken languages in the world and the official language in 26 countries), because there is a serious gap in the literature on speech recognition systems for Arabic, making the potential impact high. The investigation covers a variety of tasks, including the implementation of the first reservoir-based Arabic speech recognition system. In addition, a thorough evaluation of the developed system is conducted including several comparisons to other state- of-the-art models found in the literature, and baseline models. The impact of feature extraction methods are studied in this work, and a new biologically inspired feature extraction technique, namely the Auditory Nerve feature, is applied to the speech recognition domain. Comparing different feature extraction methods requires access to the original recorded sound, which is not possible in the only publicly accessible Arabic corpus. We have developed the largest public Arabic corpus for isolated words, which contains roughly 10,000 samples. Our investigation has led us to develop two novel approaches based on reservoir computing, ESNSVMs (Echo State Networks with Support Vector Machines) and ESNEKMs (Echo State Networks with Extreme Kernel Machines). These aim to improve the performance of the conventional RC approach by proposing different readout architectures. These two approaches have been compared to the conventional RC approach and other state-of-the- art systems. Finally, these developed approaches have been evaluated on the presence of different types and levels of noise to examine their resilience to noise, which is crucial for real world applications