Michael Feld

The automotive ontology: managing knowledge inside the vehicle and sharing it between cars

Cars have been increasingly equipped with technology, meeting the demand of people for safety, connectivity, and comfort. Upcoming HMIs provide access to in-car systems and web services in a personalized manner that facilitates a large array of functionality even while driving, with other passengers also benefiting from an enhanced experience. Such intelligent applications however depend on a solid basis to be effective: Personalization, adaptive HMI, situation-aware intelligent systems -- either of these require semantic knowledge about the user, the vehicle, the current driving situation. Advanced functions coexist with sensors, other functions, and even other vehicles. In such an environment, collaboration can be highly beneficial. Obtaining a common understanding of knowledge and providing a platform to exchange it is essential in order to reach the next level of intelligent in-car systems. This work describes the Automotive Ontology, which is located at the core of such an open platform. We give an overview of design areas relevant to automotive applications, as well as meta aspects that facilitate inference and reasoning.

Combining regression and classification methods for improving automatic speaker age recognition

We present a novel approach to automatic speaker age classification, which combines regression and classification to achieve competitive classification accuracy on telephone speech. Support vector machine regression is used to generate finer age estimates, which are combined with the posterior probabilities of well-trained discriminative gender classifiers to predict both the age and gender of a speaker. We show that this combination performs better than direct 7-class classifiers. The regressors and classifiers are trained using longterm features such as pitch and formants, as well as short-term (frame-based) features derived from MAP adaptation of GMMs that were trained on MFCCs.

Personalized In-Vehicle Information Systems: Building an Application Infrastructure for Smart Cars in Smart Spaces

Although intelligent components in modern cars help to contribute to safe mobility, they lack important Smart Environment characteristics like personalization. Moreover, todays in-vehicle infotainment systems do not offer any interaction possibility between the passenger and the visible environment around the car. In this paper we combine several technologies and propose an approach on personalized user interaction in urban environments. We present a showcase that points out the interplay of personalized in-vehicle infotainment system and interaction with visible outside environment.

Multilingual speaker age recognition: Regression analyses on the Lwazi corpus

Multilinguality represents an area of significant opportunities for automatic speech-processing systems: whereas multilingual societies are commonplace, the majority of speech-processing systems are developed with a single language in mind. As a step towards improved understanding of multilingual speech processing, the current contribution investigates how an important para-linguistic aspect of speech, namely speaker age, depends on the language spoken. In particular, we study how certain speech features affect the performance of an age recognition system for different South African languages in the Lwazi corpus. By optimizing our feature set and performing language-specific tuning, we are working towards true multilingual classifiers. As they are closely related, ASR and dialog systems are likely to benefit from an improved classification of the speaker. In a comprehensive corpus analysis on long-term features, we have identified features that exhibit characteristic behaviors for particular languages. In a follow-up regression experiment, we confirm the suitability of our feature selection for age recognition and present cross-language error rates. The mean absolute error ranges between 7.7 and 12.8 years for same-language predictors and rises to 14.5 years for cross-language predictors.

This is me: using ambient voice patterns for in-car positioning

With the range of services that can be accessed inside a car constantly increasing, so are the opportunities for personalizing the experience for both driver and other passengers. A main challenge however is to find out who is sitting where without asking explicitly. The solution presented in this paper combines two sources of information in a novel way: Ambient speech and mobile personal devices. The approach offers improved privacy by putting the user in control, and it does not require specialized positioning technologies such as RFID. In a data-driven evaluation, we confirm that the accuracy is sufficient to support a ten-speaker scenario in practice.

SiAM-dp: A Platform for the Model-Based Development of Context-Aware Multimodal Dialogue Applications

Intelligent Environments are highly interactive by integrating information and communication technology into the physical space. One goal is to provide user interfaces that are adaptive to the user and the environmental context, including the communication modalities. We present a new development platform for multimodal dialogue systems. A development approach based on semantic-models supports the creation of situation aware dialogue applications in a declarative way.

Hybrid Teams of Humans, Robots, and Virtual Agents in a Production Setting

This video paper describes the practical outcome of the first milestone of a project aiming at setting up a so-called Hybrid Team that can accomplish a wide variety of different tasks. In general, the aim is to realize and examine the collaboration of augmented humans with autonomous robots, virtual characters and SoftBots (purely software based agents) working together in a Hybrid Team to accomplish common tasks. The accompanying video shows a customized packaging scenario and can be downloaded from http://hysociatea.dfki.de/?p=441.

Combining Speech, Gaze, and Micro-gestures for the Multimodal Control of In-Car Functions

Modern cars are already incredibly smart environments today due to the sheer number of sensors and processors packed into a small space. Likewise, new technologies in human-computer interaction increasingly find their way inside, e.g. eye tracking, speech interaction and gesture recognition. The support of new modalities is promising a reduction of driver distraction and a better handling of an increasing number of functions offered by in-vehicle systems. With multiple modalities to choose from, which can be combined arbitrarily via multimodal fusion, drivers can make a free choice depending on the demands of the situation and their preferences. Our paper presents a prototype in-car system that allows car features (like turning lights and windows) to be controlled by combinations of speech, gaze, and micro-gestures. We propose an interaction concept, sketch our architecture based on a domain-independent multimodal dialogue platform, and draw some first conclusions on the outcome.

Generating a Personalized UI for the Car: A User-Adaptive Rendering Architecture

Personalized systems are gaining popularity in various mobile scenarios. In this work, we take on the challenges associated with the automotive domain and present a user-adaptive graphical renderer. By supporting a strictly model-based development processes, we meet the rigid requirements of the industry. The proposed architecture is based on the UIML standard and a novel rule-based adaptation framework.

2nd multimodal interfaces for automotive applications (MIAA 2010)

This paper summarizes the main objectives of the 2nd IUI workshop on multimodal interfaces for automotive applications (MIAA 2010).