Implementation of a Biometric Interface in Voice Controlled Wheelchairs

In order to assist physically handicapped persons in their movements,we developed an embedded isolated word speech recognition system(ASR)applied to voice control of smart wheelchairs.However,in spite of the existence in the industrial market of several kinds of electric wheelchairs,the problem remains the need to manually control this device by hand via joystick;which limits their use especially by people with severe disabilities.Thus,a significant number of disabled people cannot use a standard electric wheelchair or drive it with difficulty.The proposed solution is to use the voice to control and drive the wheelchair instead of classical joysticks.The intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors,a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP card.The ASR architecture consists of two main modules.The first one is the speech parameterization module(features extraction)and the second module is the classifier which identifies the speech and generates the control word to motors power unit.The training and recognition phases are based on Hidden Markov Models(HMM),K-means,Baum-Welch and Viterbi algorithms.The database consists of 39 isolated speaker words(13 words pronounced 3 times under different environments and conditions).The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP kit.The results and experiments obtained gave promising recognition ratio and accuracy around 99%in clean environment.However,the system accuracy decreases considerably in noisy environments,especially for SNR values below 5 dB(in street:78%,in factory:52%).

Speech-Music-Noise Discrimination in Sound Indexing of Multimedia Documents

Sound indexing and segmentation of digital documentsespecially in the internet and digital libraries are very useful tosimplify and to accelerate the multimedia document retrieval. Wecan imagine that we can extract multimedia files not only bykeywords but also by speech semantic contents. The maindifficulty of this operation is the parameterization and modellingof the sound track and the discrimination of the speech, musicand noise segments. In this paper, we will present aSpeech/Music/Noise indexing interface designed for audiodiscrimination in multimedia documents. The program uses astatistical method based on ANN and HMM classifiers. After preemphasisand segmentation, the audio segments are analysed bythe cepstral acoustic analysis method. The developed system wasevaluated on a database constituted of music songs with Arabicspeech segments under several noisy environments.

Multiscale Product for Edge Detection

Many methods have been proposed to extract the most relevant areas of an image. This article explores the method of edge detection by the multiscale product （MP） of the wavelet transform. The wavelet used in this work is the first derivative of a bidimensional Gaussian function. InitiaRy, we construct the wavelet, then we present the MP approach which is applied to binary and grey levels images. This method is compared with other methods without noise and in the presence of noise. The experiment results show fhht the MP method for edge detection outPerforms conventional methods even in noisy environments.