Eye4Ref is a rich multimodal dataset of eye-movement recordings collected from referentially complex situated settings where the linguistic utterances and their visual referential world were available to the listener. It consists of not only fixation parameters but also saccadic movement parameters that are time-locked to accompanying German utterances (with English translations). Additionally, it also contains symbolic knowledge (contextual) representations of the images to map the referring expressions onto the objects in corresponding images. Overall, the data was collected from 62 participants in three different experimental setups (86 systematically controlled sentence–image pairs and 1844 eye-movement recordings). Referential complexity was controlled by visual manipulations (e.g. number of objects, visibility of the target items, etc.), and by linguistic manipulations (e.g., the position of the disambiguating word in a sentence). This multimodal dataset, in which the three different sources of information namely eye-tracking, language, and visual environment are aligned, offers a test of various research questions not from only language perspective but also computer vision.

Abstract This paper presents morpheme-based language models developed for Amharic (a morphologically rich Semitic language) and their application to a speech recognition task. A substantial reduction in the out of vocabulary rate has been observed as a result of using subwords or morphemes. Thus a severe problem of morphologically rich languages has been addressed. Moreover, lower perplexity values have been obtained with morpheme-based language models than with word-based models. However, when comparing the quality based on the probability assigned to the test sets, word-based models seem to fare better. We have studied the utility of morpheme-based language models in speech recognition systems and found that the performance of a relatively small vocabulary (5k) speech recognition system improved significantly as a result of using morphemes as language modeling and dictionary units. However, as the size of the vocabulary increases (20k or more) the morpheme-based systems suffer from acoustic confusability and did not achieve a significant improvement over a word-based system with an equivalent vocabulary size even with the use of higher order (quadrogram) n-gram language models.

Automotive radars are on the market since 1999, both in the frequency range around 24 GHz as well as 76.5 GHz, with a new frequency band ranging from 77 to 81 GHz intended for medium and short-range sensors. The choice and design of the respective sensor antennas are determined by the requirement for high gain and low loss combined with small size and depth for vehicle integration, the challenges by the millimeter-wave frequency range, and a great cost pressure for this commercial application. Consequently, planar antennas are dominating in the lower frequency range, while lens and reflector antennas had been the first choice at 76.5 GHz, partly in folded configurations. With increasing requirements towards a much more detailed observation of the scenery in front or around the vehicle, multibeam antennas or scanning antennas have been designed, and solutions based on (digital) beamforming with a number of integrated antennas are in use or under development. General antenna concepts, partly including some system aspects, as well as three realized antenna configurations will be described in detail in this contribution.

In this paper we present the development of an Automatic Speech Recognition System (ASRS) for Amharic using limited available resources and the freely available speech toolkit (HTK). There are phonological, dialectal, orthographic and morphological features of Amharic that challenge the development of ASRSs. The problem of resource scarcity is also a hindrance to the research and development initiatives in the area of Amharic ASR. Dealing with these language and resource related problems, we have developed syllableand triphone-based ASR for Amharic and achieved 90.43% and 91.31% word recognition accuracy, respectively, on the evaluation test set of 5k vocabulary.

We investigate the utility of supertag information for guiding an existing dependency parser of German. Using weighted constraints to integrate the additionally available information, the decision process of the parser is influenced by changing its preferences, without excluding alternative structural interpretations from being considered. The paper reports on a series of experiments using varying models of supertags that significantly increase the parsing accuracy. In addition, an upper bound on the accuracy that can be achieved with perfect supertags is estimated.

In this paper we investigate the benefit of stochastic predictor components for the parsing quality which can be obtained with a rule-based dependency grammar. By including a chunker, a supertagger, a PP attacher, and a fast probabilistic parser we were able to improve upon the baseline by 3.2%, bringing the overall labelled accuracy to 91.1% on the German NEGRA corpus. We attribute the successful integration to the ability of the underlying grammar model to combine uncertain evidence in a soft manner, thus avoiding the problem of error propagation.

A compact 24 GHz sensor setup for experimental verifications of radar imaging and digital beam-forming has been designed and realized. The sensor consists of a FM-CW radar front-end and a remote radar electronic unit. The front-end consists of a switched antenna array of 25 linearly arranged elements fed by one transmitter and two parallel homodyne receivers connected to separate receive antenna elements. The remote radar electronic unit includes the base-band signal generation, analog inputs for data acquisition and programmable digital circuitry for measurement control, data storage and interface handling. The sensor can be operated from a notebook computer via standard PC interfaces, thus forming a compact imaging radar system well suited for field tests. The sensor addresses primarily short and medium range automotive applications, but its flexibility in signal generation, sampling and coherent signal processing makes it interesting for many other application fields. In this article, a detailed description of the total system is given and exemplary signal processing methods for imaging and beam-forming are introduced and demonstrated with measurement results

A collision avoidance and warning system for civil helicopters in Japan has been developed. A real-time warning method was applied using an image fused from a coloured video, an IR sensor data and mm-wave radar measurements. This paper focuses on the FM-CW mm-wave radar sensor and the associated antennas, working at 94 GHz. Flight tests were conducted in order to detect power lines. Thanks to two different antennas, power line response was measured in vertical and horizontal polarizations. Maximum detection ranges are of 800 and 600m respectively. This variation comes from the antenna gain difference. Radar resolution is limited by the allocated bandwidth at 94 GHz which is 200 MHz. Comparisons were conducted on the ground with an integrated FM-CW radar working at 77 GHz and achieving 500 MHz bandwidth

Amharic is the official language of Ethiopia. It belongs to the Semitic language family and is characterized by a quite homogeneous phonology distinguishing between 234 distinct Consonant-Vowel (CV) syllables.

Based on constraint optimization techniques, an architecture for robust parsing of natural language utterances has been developed. The resulting system is able to combine possibly contradicting evidence from a variety of information sources, using a plausibility-based arbitration procedure to derive fairly rich structural representations, comprising aspects of syntax, semantics and other description levels of language. The results of a series of experiments are reported which demonstrate the high potential for robust behaviour with respect to ungrammaticality, incomplete utterances, and temporal pressure.

This paper is a case study of user involvement in the requirements specification for project ISLE: Interactive Spoken Language Education. Developers of Spoken Language Dialogue Systems should involve users from the outset, particularly if the aim is to develop novel solutions for a generic target application area or market. As well as target end-users, SLDS developers should identify and consult ‘meta-level’ domain experts with expertise in human-to-human dialogue in the target domain. In our case, English language teachers and publishers provided generic knowledge of learners' dialogue preferences; other applications have analogous domain language experts. These domain language experts can help to pin down a domain-specific sublanguage which fits the constraints of current speech recognition technology: linguistically-naive end-users may expect unconstrained conversational English, but in practice, dialogue interactions have to be constrained in vocabulary and syntax. User consultation also highlighted a need to consider how to integrate speech input and output with other modes of interaction and processing; in our case the input speech signal is processed by speech recogniser, stress and mispronunciation detectors, and output responses are text and graphics as well as speech. This suggests a need to revisit the definition of ‘dialogue’: other SLDS developers should also consider the merits of multimodality as an adjunct to pure spoken language dialogue, particularly given that current systems are not capable of accurately handling unconstrained English.

. The eliminative nature of constraint satisfaction over finite domains offers an interesting potentialfor robustness in the parsing of spoken language. An approach is presented that puts unusually ambitious demands on the design of the constraint satisfaction procedure by trying to combine preferential reasoning, dynamic scheduling, parallel processing, and incremental constraint solving, within a coherent solution.