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.

Emotion Recognition from Speech with Acoustic, Non-Linear and Wavelet-based Features Extracted in Different Acoustic Conditions

ABSTRACT: In the last years, there has a great progress in automatic speech recognition. The challenge now it is not only recognize the semantic content in the speech but also the called "paralinguistic" aspects of the speech, including the emotions, and the personality of the speaker. This research work aims in the development of a methodology for the automatic emotion recognition from speech signals in non-controlled noise conditions. For that purpose, different sets of acoustic, non-linear, and wavelet based features are used to characterize emotions in different databases created for such purpose

Comparison of CELP speech coder with a wavelet method

This thesis compares the speech quality of Code Excited Linear Predictor (CELP, Federal Standard 1016) speech coder with a new wavelet method to compress speech. The performances of both are compared by performing subjective listening tests. The test signals used are clean signals (i.e. with no background noise), speech signals with room noise and speech signals with artificial noise added. Results indicate that for clean signals and signals with predominantly voiced components the CELP standard performs better than the wavelet method but for signals with room noise the wavelet method performs much better than the CELP. For signals with artificial noise added, the results are mixed depending on the level of artificial noise added with CELP performing better for low level noise added signals and the wavelet method performing better for higher noise levels

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Speech Detection Using Gammatone Features And One-class Support Vector Machine

A network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD’s rely on time-domain features and simple thresholds for efficient speech detection however this doesn’t say much about the signal being passed. More sophisticated methods employ machine learning algorithms, but train on specific noises intended for a target environment. Validating speech under a variety of unknown conditions must be possible; as well as differentiating between speech and nonspeech data embedded within the packets. A real-time speech detection method is proposed that relies only on a clean speech model for detection. Through the use of Gammatone filter bank processing, the Cepstrum and several frequency domain features are used to train a One-Class Support Vector Machine which provides a clean-speech model irrespective of environmental noise. A Wiener filter is used to provide improved operation for harsh noise environments. Greater than 90% detection accuracy is achieved for clean speech with approximately 70% accuracy for SNR as low as 5d

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

A full order sliding mode tracking controller design for an electrohydraulic control system

Electrohydraulic control system are widely use in industry due to continuous operation, higher speed of response with fast motion etc. However, there is a drawback that it is difficult to control because of the highly nonlinear and parameters uncertainties. In this project, a Full Order Sliding Mode Controller is design to control the system. First, the mathematical model of the electrohydraulic servo control system is developed. Then the mathematic model will be transformed into state space representation for the purposed of designing the controller. The system will be treated as an uncertain system with bounded uncertainties where the bounded are assumed known. The proposed controller will be designed based on deterministic approach, such that the overall system is practically stable and tracks the desired trajectory in spite the uncertainties and nonlinearities present in the system. The performance and reliability of the proposal controller will be determined by performing extensive simulation using MATLAB/SIMULINK. Lastly, the performance of the controller is to be compared with Independent Joint Linear Control and advanced deterministic controller

Improving Quality of Life: Home Care for Chronically Ill and Elderly People

In this chapter, we propose a system especially created for elderly or chronically ill people that are with special needs and poor familiarity with technology. The system combines home monitoring of physiological and emotional states through a set of wearable sensors, user-controlled (automated) home devices, and a central control for integration of the data, in order to provide a safe and friendly environment according to the limited capabilities of the users. The main objective is to create the easy, low-cost automation of a room or house to provide a friendly environment that enhances the psychological condition of immobilized users. In addition, the complete interaction of the components provides an overview of the physical and emotional state of the user, building a behavior pattern that can be supervised by the care giving staff. This approach allows the integration of physiological signals with the patient’s environmental and social context to obtain a complete framework of the emotional states