The CLEAR 2007 Evaluation

Abstract. This paper is a summary of the 2007 CLEAR Evaluation on the Classification of Events, Activities, and Relationships which took place in early 2007 and culminated with a two-day workshop held in May 2007. CLEAR is an international effort to evaluate systems for the perception of people, their activities, and interactions. In its second year, CLEAR has developed a following from the computer vision and speech communities, spawning a more multimodal perspective of research eval-uation. This paper describes the evaluation tasks, including metrics and databases used, and discusses the results achieved. The CLEAR 2007 tasks comprise person, face, and vehicle tracking, head pose estimation, as well as acoustic scene analysis. These include subtasks performed in the visual, acoustic and audio-visual domains for meeting room and surveillance data.

Deliverable D7.4 Project demonstrator v1

LinkedTV is a project implementing innovative technology and services at every step of the media workflow, in order to enable a seamless interlinking of Web and TV content for the consumer. This technology needs to be seen and to be communicated, hence besides implementation also dissemination is important for the project R&D. This demonstrator collects all project R&D results into a single document, reflecting our online listings of tools, services and demos which are kept continually up to date

PHONOTACTIC AND ACOUSTIC LANGUAGE RECOGNITION

Práce pojednává o fonotaktickém a akustickém přístupu pro automatické rozpoznávání jazyka. První část práce pojednává o fonotaktickém přístupu založeném na výskytu fonémových sekvenci v řeči. Nejdříve je prezentován popis vývoje fonémového rozpoznávače jako techniky pro přepis řeči do sekvence smysluplných symbolů. Hlavní důraz je kladen na dobré natrénování fonémového rozpoznávače a kombinaci výsledků z několika fonémových rozpoznávačů trénovaných na různých jazycích (Paralelní fonémové rozpoznávání následované jazykovými modely (PPRLM)). Práce také pojednává o nové technice anti-modely v PPRLM a studuje použití fonémových grafů místo nejlepšího přepisu. Na závěr práce jsou porovnány dva přístupy modelování výstupu fonémového rozpoznávače -- standardní n-gramové jazykové modely a binární rozhodovací stromy. Hlavní přínos v akustickém přístupu je diskriminativní modelování cílových modelů jazyků a první experimenty s kombinací diskriminativního trénování a na příznacích, kde byl odstraněn vliv kanálu. Práce dále zkoumá různé druhy technik fúzi akustického a fonotaktického přístupu. Všechny experimenty jsou provedeny na standardních datech z NIST evaluaci konané v letech 2003, 2005 a 2007, takže jsou přímo porovnatelné s výsledky ostatních skupin zabývajících se automatickým rozpoznáváním jazyka. S fúzí uvedených technik jsme posunuli state-of-the-art výsledky a dosáhli vynikajících výsledků ve dvou NIST evaluacích.This thesis deals with phonotactic and acoustic techniques for automatic language recognition (LRE). The first part of the thesis deals with the phonotactic language recognition based on co-occurrences of phone sequences in speech. A thorough study of phone recognition as tokenization technique for LRE is done, with focus on the amounts of training data for phone recognizer and on the combination of phone recognizers trained on several language (Parallel Phone Recognition followed by Language Model - PPRLM). The thesis also deals with novel technique of anti-models in PPRLM and investigates into using phone lattices instead of strings. The work on phonotactic approach is concluded by a comparison of classical n-gram modeling techniques and binary decision trees. The acoustic LRE was addressed too, with the main focus on discriminative techniques for training target language acoustic models and on initial (but successful) experiments with removing channel dependencies. We have also investigated into the fusion of phonotactic and acoustic approaches. All experiments were performed on standard data from NIST 2003, 2005 and 2007 evaluations so that the results are directly comparable to other laboratories in the LRE community. With the above mentioned techniques, the fused systems defined the state-of-the-art in the LRE field and reached excellent results in NIST evaluations.

Change Detection in Stochastic Shape Dynamical Models with Applications in Activity Modeling and Abnormality Detection

The goal of this research is to model an ``activity" performed by a group of moving and interacting objects (which can be people or cars or robots or different rigid components of the human body) and use these models for abnormal activity detection, tracking and segmentation. Previous approaches to modeling group activity include co-occurrence statistics (individual and joint histograms) and Dynamic Bayesian Networks, neither of which is applicable when the number of interacting objects is large. We treat the objects as point objects (referred to as ``landmarks'') and propose to model their changing configuration as a moving and deforming ``shape" using ideas from Kendall's shape theory for discrete landmarks. A continuous state HMM is defined for landmark shape dynamics in an ``activity". The configuration of landmarks at a given time forms the observation vector and the corresponding shape and scaled Euclidean motion parameters form the hidden state vector. The dynamical model for shape is a linear Gauss-Markov model on shape ``velocity". The ``shape velocity" at a point on the shape manifold is defined in the tangent space to the manifold at that point. Particle filters are used to track the HMM, i.e. estimate the hidden state given observations. An abnormal activity is defined as a change in the shape activity model, which could be slow or drastic and whose parameters are unknown. Drastic changes can be easily detected using the increase in tracking error or the negative log of the likelihood of current observation given past (OL). But slow changes usually get missed. We have proposed a statistic for slow change detection called ELL (which is the Expectation of negative Log Likelihood of state given past observations) and shown analytically and experimentally the complementary behavior of ELL and OL for slow and drastic changes. We have established the stability (monotonic decrease) of the errors in approximating the ELL for changed observations using a particle filter that is optimal for the unchanged system. Asymptotic stability is shown under stronger assumptions. Finally, it is shown that the upper bound on ELL error is an increasing function of the ``rate of change" with increasing derivatives of all orders, and its implications are discussed. Another contribution of the thesis is a linear subspace algorithm for pattern classification, which we call Principal Components' Null Space Analysis (PCNSA). PCNSA was motivated by Principal Components' Analysis (PCA) and it approximates the optimal Bayes classifier for Gaussian distributions with unequal covariance matrices. We have derived classification error probability expressions for PCNSA and compared its performance with that of subspace Linear Discriminant Analysis (LDA) both analytically and experimentally. Applications to abnormal activity detection, human action retrieval, object/face recognition are discussed.% with experimental results

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

Automatic Sign Language Recognition from Image Data

Tato práce se zabývá problematikou automatického rozpoznávání znakového jazyka z obrazových dat. Práce představuje pět hlavních přínosů v oblasti tvorby systému pro rozpoznávání, tvorby korpusů, extrakci příznaků z rukou a obličeje s využitím metod pro sledování pozice a pohybu rukou (tracking) a modelování znaků s využitím menších fonetických jednotek (sub-units). Metody využité v rozpoznávacím systému byly využity i k tvorbě vyhledávacího nástroje "search by example", který dokáže vyhledávat ve videozáznamech podle obrázku ruky. Navržený systém pro automatické rozpoznávání znakového jazyka je založen na statistickém přístupu s využitím skrytých Markovových modelů, obsahuje moduly pro analýzu video dat, modelování znaků a dekódování. Systém je schopen rozpoznávat jak izolované, tak spojité promluvy. Veškeré experimenty a vyhodnocení byly provedeny s vlastními korpusy UWB-06-SLR-A a UWB-07-SLR-P, první z nich obsahuje 25 znaků, druhý 378. Základní extrakce příznaků z video dat byla provedena na nízkoúrovňových popisech obrazu. Lepších výsledků bylo dosaženo s příznaky získaných z popisů vyšší úrovně porozumění obsahu v obraze, které využívají sledování pozice rukou a metodu pro segmentaci rukou v době překryvu s obličejem. Navíc, využitá metoda dokáže interpolovat obrazy s obličejem v době překryvu a umožňuje tak využít metody pro extrakci příznaků z obličeje, které by během překryvu nefungovaly, jako např. metoda active appearance models (AAM). Bylo porovnáno několik různých metod pro extrakci příznaků z rukou, jako např. local binary patterns (LBP), histogram of oriented gradients (HOG), vysokoúrovnové lingvistické příznaky a nové navržená metoda hand shape radial distance function (hRDF). Bylo také zkoumáno využití menších fonetických jednotek, než jsou celé znaky, tzv. sub-units. Pro první krok tvorby těchto jednotek byl navržen iterativní algoritmus, který tyto jednotky automaticky vytváří analýzou existujících dat. Bylo ukázáno, že tento koncept je vhodný pro modelování a rozpoznávání znaků. Kromě systému pro rozpoznávání je v práci navržen a představen systém "search by example", který funguje jako vyhledávací systém pro videa se záznamy znakového jazyka a může být využit například v online slovnících znakového jazyka, kde je v současné době složité či nemožné v takovýchto datech vyhledávat. Tento nástroj využívá metody, které byly použity v rozpoznávacím systému. Výstupem tohoto vyhledávacího nástroje je seřazený seznam videí, které obsahují stejný nebo podobný tvar ruky, které zadal uživatel, např. přes webkameru.Katedra kybernetikyObhájenoThis thesis addresses several issues of automatic sign language recognition, namely the creation of vision based sign language recognition framework, sign language corpora creation, feature extraction, making use of novel hand tracking with face occlusion handling, data-driven creation of sub-units and "search by example" tool for searching in sign language corpora using hand images as a search query. The proposed sign language recognition framework, based on statistical approach incorporating hidden Markov models (HMM), consists of video analysis, sign modeling and decoding modules. The framework is able to recognize both isolated signs and continuous utterances from video data. All experiments and evaluations were performed on two own corpora, UWB-06-SLR-A and UWB-07-SLR-P, the first containing 25 signs and second 378. As a baseline feature descriptors, low level image features are used. It is shown that better performance is gained by higher level features that employ hand tracking, which resolve occlusions of hands and face. As a side effect, the occlusion handling method interpolates face area in the frames during the occlusion and allows to use face feature descriptors that fail in such a case, for instance features extracted from active appearance models (AAM) tracker. Several state-of-the-art appearance-based feature descriptors were compared for tracked hands, such as local binary patterns (LBP), histogram of oriented gradients (HOG), high-level linguistic features or newly proposed hand shape radial distance function (denoted as hRDF) that enhances the feature description of hand-shape like concave regions. The concept of sub-units, that uses HMM models based on linguistic units smaller than whole sign and covers inner structures of the signs, was investigated in the proposed iterative method that is a first required step for data-driven construction of sub-units, and shows that such a concept is suitable for sign modeling and recognition tasks. Except of experiments in the sign language recognition, additional tool \textit{search by example} was created and evaluated. This tool is a search engine for sign language videos. Such a system can be incorporated into an online sign language dictionary where it is difficult to search in the sign language data. This proposed tool employs several methods which were examined in the sign language recognition task and allows to search in the video corpora based on an user-given query that consists of one or multiple images of hands. As a result, an ordered list of videos that contain the same or similar hand configurations is returned

Automatic Speech Recognition Using LP-DCTC/DCS Analysis Followed by Morphological Filtering

Front-end feature extraction techniques have long been a critical component in Automatic Speech Recognition (ASR). Nonlinear filtering techniques are becoming increasingly important in this application, and are often better than linear filters at removing noise without distorting speech features. However, design and analysis of nonlinear filters are more difficult than for linear filters. Mathematical morphology, which creates filters based on shape and size characteristics, is a design structure for nonlinear filters. These filters are limited to minimum and maximum operations that introduce a deterministic bias into filtered signals. This work develops filtering structures based on a mathematical morphology that utilizes the bias while emphasizing spectral peaks. The combination of peak emphasis via LP analysis with morphological filtering results in more noise robust speech recognition rates. To help understand the behavior of these pre-processing techniques the deterministic and statistical properties of the morphological filters are compared to the properties of feature extraction techniques that do not employ such algorithms. The robust behavior of these algorithms for automatic speech recognition in the presence of rapidly fluctuating speech signals with additive and convolutional noise is illustrated. Examples of these nonlinear feature extraction techniques are given using the Aurora 2.0 and Aurora 3.0 databases. Features are computed using LP analysis alone to emphasize peaks, morphological filtering alone, or a combination of the two approaches. Although absolute best results are normally obtained using a combination of the two methods, morphological filtering alone is nearly as effective and much more computationally efficient

Discriminative connectionist approaches for automatic speech recognition in cars

The first part of this thesis is devoted to the evaluation of approaches which exploit the inherent redundancy of the speech signal to improve the noise robustness. On the basis of this evaluation on the AURORA 2000 database, we further study in detail two of the evaluated approaches. The first of these approaches is the hybrid RBF/HMM approach, which is an attempt to combine the superior classification performance of radial basis functions (RBFs) with the ability of HMMs to model time variation. The second approach is using neural networks to non-linearly reduce the dimensionality of large feature vectors including context frames. We propose the use of different MLP topologies for that purpose. Experiments on the AURORA 2000 database reveal that the performance of the first approach is similar to the performance of systems based on SCHMMs. The second approach cannot outperform the performance of linear discriminant analysis (LDA) on a database recorded in real car environments, but it is on average significantly better than LDA on the AURORA 2000 database.Im ersten Teil dieser Arbeit werden bestehende Verfahren zur Erhöhung der Robustheit von Spracherkennungssystemen in lauten Umgebungen evaluiert, die auf der Ausnutzung der Redundanz im Sprachsignal basieren. Auf der Grundlage dieser Evaluation auf der AURORA 2000 Datenbank werden zwei spezielle Ansätze weiter ausgearbeitet und detalliert analysiert. Der erste dieser Ansätze verbindet die herausragende Klassifikationsleistung von neuronalen Netzen mit radialen Basisfunktionen (RBF) mit der Fähigkeit von Hidden-Markov-Modellen (HMM), Zeitveränderlichkeiten zu modellieren. In einem zweiten Ansatz werden NN zur nichtlinearen Dimensionsreduktion hochdimensionaler Kontextvektoren in unterschiedlichen Netzwerk-Topologien untersucht. In Experimenten konnte gezeigt werden, dass der erste dieser Ansätze für die AURORA-Datenbank eine ähnliche Leistungsfähigkeit wie semikontinuierliche HMM (SCHMM) aufweist. Der zweite Ansatz erzielt auf einer im Kraftfahrzeug aufgenommenen Datenbank keine Verbesserung gegenüber den klassischen linearen Ansätzen zu Dimensionsreduktion (LDA), erweist sich aber auf der AURORA-Datenbank als signifikan