Realization and design of a pilot assist decision-making system based on speech recognition

A system based on speech recognition is proposed for pilot assist decision-making. It is based on a HIL aircraft simulation platform and uses the microcontroller SPCE061A as the central processor to achieve better reliability and higher cost-effect performance. Technologies of LPCC (linear predictive cepstral coding) and DTW (Dynamic Time Warping) are applied for isolated-word speech recognition to gain a smaller amount of calculation and a better real-time performance. Besides, we adopt the PWM (Pulse Width Modulation) regulation technology to effectively regulate each control surface by speech, and thus to assist the pilot to make decisions. By trial and error, it is proved that we have a satisfactory accuracy rate of speech recognition and control effect. More importantly, our paper provides a creative idea for intelligent human-computer interaction and applications of speech recognition in the field of aviation control. Our system is also very easy to be extended and applied.Comment: 10 pages, 8 figure

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

Video based system monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (p. 207-216).In this work we develop new algorithms for video comparison, for video alignment, and for determining the similarity between entire video clips or detecting similarities between sub-videos. The intent of this work is to develop video-based techniques for autonomous monitoring of systems in industrial, manufacturing, and research environments. We develop an algorithm, Dynamic Time and Space Warping, to determine a model-free similarity between an example and an unknown video. The algorithm optimally shifts space and warps time according to local measures of video similarity. The resulting similarity measure is an optimal path of similarity versus space and time used to optimally align or compare the two video. We demonstrate the applicability of such similarity measures to industrial wear monitoring, failure prediction, and assembly-line feedback control and to non-industrial settings with examples in sports and entertainment. We extend the similarity machinery and introduce a new technique for video event-detection. The local similarity is integrated along the optimal space-time path in order to determine a cumulative similarity.(cont.) We demonstrate the applicability to content query and surveillance; we identify the temporal and spatial location inside of a large video stream which is similar to a query, or template, video. We explore applications in video classification. We investigate the performance degradation and robustness of the algorithms to noise via distortion of real examples and simulation. We develop techniques to aid engineers in the selection of a video template that is relevant to their monitoring application and locally robust to noise. We explore the structure and computational complexity of the algorithms. We demonstrate that the algorithms are highly-parallelizable and that the fast processing rates necessary for many industrial monitoring applications are achievable.by Brian W. Anthony.Ph.D

On design and implementation of an Embedded automatic speech recognition system

We present a new design of an Embedded Speech Recognition System. It combines the aspects of both hardware and software design to implement a speaker dependent, isolated word, small vocabulary speech recognition system. The feature extraction is based on modified Mel-scaled Frequency Cepstral Coefficients (MFCC) and template matching employs Dynamic Time Warping (DTW). A novel algorithm has been used to improve the detection of start of a word. The hardware is built around the industry standard TMS320LF2407A DSP. The board is designed to serve as a general purpose DSP development board for the 24X series of TI DSPs. It contains, apart from the DSR the external SRAM, FLASH, ADC interface, I/O interfacing blocks and JTAG interface. Both the hardware and the software have been designed concurrently, with a view to achieve high-speed recognition with maximum accuracy in minimum power and making the device portable. The proposed solution is a low-cost, high-performance, scalable alternative to other existing products