应急疏散语音告警信号设计参数的研究

Voice alarm plays an important role in emergency evacuation of public place, because it can provide information and instruct evacuation. This paper studied the optimization of acoustic and semantic parameters of voice alarms in emergency evacuation, so that alarm design can improve the evacuation performance. Both method of magnitude estimation and scale were implemented to investigate participants' perceived urgency of the alarms with different parameters. The results indicated that, participants evaluated the alarms with faster speech rate, with greater signal to noise ratio (SNR) and under louder noises more urgent. There was an interaction between noise level and content of voice alarm. Signals with speech rate below 4 characters / second were evaluated as non urgent at all. Intelligibility of the voice alarm was investigated by evaluating the key pointed recognition performance. The results showed that, speech rate’s effect was a marginal significance, and 7 characters / second has the highest intelligibility. It might because that the faster the signal spoken, the more attention was paid. Gender of speaker and SNR did not have a significant effect on the signals’ intelligibility. This paper also investigated impact of voice alarms' content on human behavior in emergency evacuation in a 3-D virtual reality environment. In condition of "telling the occupants what had happened and what to do", the number of participants who succeeded in evacuation was the largest. Further study, in which similar numbers of participants evacuate successfully in three conditions, indicated that the reaction time and evacuation time was the shortest in the aforesaid condition. Although one-way ANOVA shows that the difference was not significant, the results still provided some reference to the alarm design. In sum, parameters of voice alarm in emergency evacuation should be chosen to meet needs from both perceived urgency and intelligibility. Contents of the alarms should include "what had happened and what to do", and should vary according to noise levels in different public places.

Information geometry theory of high-dimension space and application for speaker independent continuous digit speech recognition

In the light of descriptive geometry and notions in set theory, this paper re-defines the basic elements in space such as curve and surface and so on, presents some fundamental notions with respect to the point cover based on the High-dimension space (HDS) point covering theory, finally takes points from mapping part of speech signals to HDS, so as to analyze distribution information of these speech points in HDS, and various geometric covering objects for speech points and their relationship. Besides, this paper also proposes a new algorithm for speaker independent continuous digit speech recognition based on the HDS point dynamic searching theory without end-points detection and segmentation. First from the different digit syllables in real continuous digit speech, we establish the covering area in feature space for continuous speech. During recognition, we make use of the point covering dynamic searching theory in HDS to do recognition, and then get the satisfying recognized results. At last, compared to HMM (Hidden Markov models)-based method, from the development trend of the comparing results, as sample amount increasing, the difference of recognition rate between two methods will decrease slowly, while sample amount approaching to be very large, two recognition rates all close to 100% little by little. As seen from the results, the recognition rate of HDS point covering method is higher than that of in HMM (Hidden Markov models) based method, because, the point covering describes the morphological distribution for speech in HDS, whereas HMM-based method is only a probability distribution, whose accuracy is certainly inferior to point covering.

crossmodal error correction of continuous handwriting recognition by speech

In recognition-based user interface, users’ satisfaction is determined not only by recognition accuracy but also by effort to correct recognition errors. In this paper, we introduce a crossmodal error correction technique, which allows users to correct errors of Chinese handwriting recognition by speech. The focus of the paper is a multimodal fusion algorithm supporting the crossmodal error correction. By fusing handwriting and speech recognition, the algorithm can correct errors in both character extraction and recognition of handwriting. The experimental result indicates that the algorithm is effective and efficient. Moreover, the evaluation also shows the correction technique can help users to correct errors in handwriting recognition more efficiently than the other two error correction techniques.