We evaluated passive radio-frequency identification (RFID) technology for detecting the use of objects and related activities during trauma resuscitation. Our system consists of RFID tags and antennas, optimally placed for object detection, as well as algorithms for processing RFID data to infer object use. To evaluate our approach, we tagged 81 objects in the resuscitation room and recorded RFID signal strength during 32 simulated resuscitations performed by trauma teams. We then analyzed RFID data to identify cues for recognizing resuscitation activities. Using these cues, we extracted descriptive features and applied machine-learning techniques to monitor interactions with objects. Our results show that an instance of a used object can be detected with accuracy rates greater than 90 percent in a crowded and fast-paced medical setting using off-the-shelf RFID equipment, and the time and duration of use can be identified with up to 83 percent accuracy. We conclude with insights into the limitations of passive RFID and areas in which RFID needs to be complemented with other sensing technologies.

We examined configuring a radio frequency identification (RFID) equipment for the best object use detection in a trauma bay. Unlike prior work on RFID, we 1) optimized the accuracy of object use detection rather than just object detection; and 2) quantitatively assessed antenna placement while addressing issues specific to tag placement likely to occur in a trauma bay. Our design started with an analysis of the environment requirements and constraints. We designed several antenna setups with different number of components (RFID tags or antennas) and their orientations. Setups were evaluated under scenarios simulating a dynamic medical setting. We used three metrics with increasing complexity and bias: read rate, received signal strength indication distribution distance, and target application performance. Our experiments showed that antennas above the regions with high object density are most suitable for detecting object use. We explored tagging strategies for challenging objects so that sufficient readout rates are obtained for computing evaluation metrics. Among the metrics, distribution distance was correlated with target application performance, and also less biased and simpler to calculate, which made it an excellent metric for context-aware applications. We present experimental results obtained in the real trauma bay to validate our findings.

This paper presents our work on the retrieval of spoken information in Turkish. Traditional speech retrieval systems perform indexing and retrieval over automatic speech recognition (ASR) transcripts, which include errors either because of out-of-vocabulary (OOV) words or ASR inaccuracy. We use subword units as recognition and indexing units to reduce the OOV rate and index alternative recognition hypotheses to handle ASR errors. Performance of such methods is evaluated on our Turkish Broadcast News Corpus with two types of speech retrieval systems: a spoken term detection (STD) and a spoken document retrieval (SDR) system. To evaluate the SDR system, we also build a spoken information retrieval (IR) collection, which is the first for Turkish. Experiments showed that word segmentation algorithms are quite useful for both tasks. SDR performance is observed to be less dependent on the ASR component, whereas any performance change in ASR directly affects STD. We also present extensive analysis of retrieval performance depending on query length, and propose length-based index combination and thresholding strategies for the STD task. Finally, a new approach, which depends on the detection of stems instead of complete terms, is tried for STD and observed to give promising results. Although evaluations were performed in Turkish, we expect the proposed methods to be effective for similar languages as well.

Speech Retrieval systems utilize automatic speech recognition (ASR) to generate textual data for indexing. However, automatic transcriptions include errors, either because of out-of-vocabulary (OOV) words or due to ASR inaccuracy. In this work, we address spoken information retrieval in Turkish, a morphologically rich language where OOV rates are high. We apply several techniques, such as using subword units and indexing alternative hypotheses, to cope with the OOV problem and ASR inaccuracy.

This paper summarizes our recent efforts for building a Turkish Broadcast News transcription and retrieval system. The agglutinative nature of Turkish leads to a high number of out-of-vocabulary (OOV) words which in turn lower automatic speech recognition (ASR) accuracy. This situation compromises the performance of speech retrieval systems based on ASR output. Therefore using a word-based ASR is not adequate for transcribing speech in Turkish. To alleviate this problem, various sub-word-based recognition units are utilized. These units solve the OOV problem with moderate size vocabularies and perform even better than a 500 K word vocabulary as far as recognition accuracy is concerned. As a novel approach, the interaction between recognition units, words and sub-words, and discriminative training is explored. Sub-word models benefit from discriminative training more than word models do, especially in the discriminative language modeling framework. For speech retrieval, a spoken term detection system based on automata indexation is utilized. As with transcription, retrieval performance is measured under various schemes incorporating words and sub-words. Best results are obtained using a cascade of word and sub-word indexes together with term-specific thresholding.

In this paper, we present a baseline spoken term detection (STD) system for Turkish broadcast news. The agglutinative structure of Turkish causes a high out-of-vocabulary (OOV) rate and increases word error rate (WER) in automatic speech recognition. Several approaches are attempted to reduce this negative effect on the STD system. Sub-word units are used to handle the OOV queries and lattice-based indexing is used to obtain different operating points and handle high WER cases. A recently proposed method for setting term specific thresholds is also evaluated and extended to allow us to choose an operating point suitable for our needs. Best results are obtained by using a cascade of word and sub-word lattice indices with term-thresholding.