Morphological Segmentation for Keyword Spotting

We explore the impact of morphological segmentation on keyword spotting (KWS). Despite potential benefits, state-of-the-art KWS systems do not use morphological information. In this paper, we augment a state-of-the-art KWS system with sub-word units derived from supervised and unsupervised morphological segmentations, and compare with phonetic and syllabic segmentations. Our experiments demonstrate that morphemes improve overall performance of KWS systems. Syllabic units, however, rival the performance of morphological units when used in KWS. By combining morphological, phonetic and syllabic segmentations, we demonstrate substantial performance gains.United States. Intelligence Advanced Research Projects Activity (United States. Army Research Laboratory Contract W911NF-12-C-0013

Customer Behaviour Analysis for Recommendation of Supermarket Ware

Part 10: Mining Humanistic Data Workshop (MHDW)International audienceIn this paper, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon Company, both containing information about customers’ purchases. Subsequently, our model analyzes these data in order to classify customers as well as products; whereas being trained and validated with real data. This model is targeted towards classifying customers according to their consuming behaviour and consequently propose new products more likely to be purchased by them. The corresponding prediction model is intended to be utilized as a tool for marketers so as to provide an analytically targeted and specified consumer behavior

Lattice Segmentation and Minimum Bayes Risk Discriminative Training

Modeling approaches are presented that incorporate discriminative training procedures in segmental Minimum Bayes-Risk decoding (SMBR). SMBR is used to segment lattices produced by a general automatic speech recognition (ASR) system into sequences of separate decision problems involving small sets of confusable words. We discuss two approaches to incorporating these segmented lattices in discriminative training. We investigate the use of acoustic models specialized to discriminate between the competing words in these classes which are then applied in subsequent SMBR rescoring passes. Refinement of the search space that allows the use of specialized discriminative models is shown to be an improvement over rescoring with conventionally trained discriminative models

Bbn Viser Trecvid 2011 Multimedia Event Detection System

We describe the Raytheon BBN (BBN) VISER system that is designed to detect events of interest in multimedia data. We also present a comprehensive analysis of the different modules of that system in the context of the MED 2011 task. The VISER system incorporates a large set of low-level features that capture appearance, color, motion, audio, and audio-visual co-occurrence patterns in videos. For the low-level features, we rigorously analyzed several coding and pooling strategies, and also used state-of-the-art spatio-temporal pooling strategies to model relationships between different features. The system also uses high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Furthermore, the VISER system exploits multimodal information by analyzing available spoken and videotext content using BBN\u27s state-of-the-art Byblos automatic speech recognition (ASR) and video text recognition systems. These diverse streams of information are combined into a single, fixed dimensional vector for each video. We explored two different combination strategies: early fusion and late fusion. Early fusion was implemented through a fast kernel-based fusion framework and late fusion was performed using both Bayesian model combination (BAYCOM) as well as an innovative a weighted-average framework. Consistent with the previous MED\u2710 evaluation, low-level visual features exhibit strong performance and form the basis of our system. However, high-level information from speech, video-text, and object detection provide consistent and significant performance improvements. Overall, BBN\u27s VISER system exhibited the best performance among all the submitted systems with an average ANDC score of 0.46 across the 10 MED\u2711 test events when the threshold was optimized for the NDC score, and \u3c30% missed detection rate when the threshold was optimized to minimize missed detections at 6% false alarm rate. Description of Submitted Runs BBNVISER-LLFeat: Uses a combination of 6 high-performing, multimodal, and complementary low-level features, namely, appearance, color, motion based, MFCC, and audio energy. We combine these low-level features using an early fusion strategy. The threshold is estimated to minimize the NDC score. BBNVISER-Fusion1: Combines several sub-systems, each based on some combination of low-level features, ASR, video text OCR, and other high-level concepts using a late-fusion, Bayesian model combination strategy. The threshold is estimated to minimize the NDC score. BBNVISER-Fusion2: Combines same set of subsystems as BBNVISER-Fusion1. Instead of BAYCOM, it uses a novel weighted average fusion strategy. The fusion weights (for each sub-system) are estimated for each video automatically at runtime. BBNVISER-Fusion3: Combines all the sub-systems used in BBNVISER-Fusion3 with separate end-to-end systems from Columbia and UCF. In all, 18 sub-systems were combined using weighted average fusion. The threshold is estimated to minimize the probability of missed detection in the neighborhood of ALADDIN\u27s Year 1 false alarm rate ceiling

Multimodal feature fusion for robust event detection in web videos. In:

Abstract Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance, color, motion, audio and audio-visual co-occurrence patterns in videos. We also evaluate the utility of high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Further, we exploit multimodal information by analyzing available spoken and videotext content using state-of-the-art automatic speech recognition (ASR) and videotext recognition systems. We combine these diverse features using a two-step strategy employing multiple kernel learning (MKL) and late score level fusion methods. Based on the TRECVID MED 2011 evaluations for detecting 10 events in a large benchmark set of ∼45000 videos, our system showed the best performance among the 19 international teams