Frank Duckhorn

Estimating Blood Sugar from Voice Samples: A Preliminary Study

Diabetes is a widespread disease. Patients have to pay attention to their blood sugar value all the time. Hypoglycemia and hyperglycemia are dangerous and must be treated. Self-testing, typically involving a prick with a lancet, is often painful and the costs for testing devices and supplies are immense. It is well-known that the human voice carries all kinds of information and depends on various factors. Therefore we conjectured that it may also be influenced by the blood sugar level. This paper presents a preliminary study which shows that a patients blood sugar condition actually seems to manifest itself in the voice. Results encourage us to go the next steps: a largescale long time study in collaboration with medical experts and, finally, the development of a measurement device or smartphone app.

Analysis-by-synthesis approach for acoustic model adaptation

This paper presents an analysis-by-synthesis approach for acoustic model adaptation. Using artificial speech data for speech recognition systems adaptation, has the potential to address the problem of data sparseness, to avoid speech recordings in real conditions and to provide the capability of performing large number of development cycles for Automatic Speech Recognition (ASR) systems in shorter time. The proposed adaptation framework uses unified ASR and synthesis system to produce artificial adaptation speech signals. In order to confirm the usability of the proposed approach, several experiments were performed where the artificial speech data was coded-decoded by different speech and waveform coders and the acoustic model used for synthesis was adapted for each coder. The recognition results show that the proposed method could be used successfully in the process of speech recognition systems performance assessment and improvement, not only for coded speech effects evaluation and adaptation, but also for other environment conditions.

A Cognitive User Interface for a Multi-modal Human-Machine Interaction

We developed a hardware-based cognitive user interface to help inexperienced and little technology-affine people to get easy access to smart home devices. The interface is able to interact (via speech, gestures, or touchscreen) with the user. By learning from the user’s behavior, it can adapt to each individual. In contrast to most commercial products, our solution keeps all data required for operation internally and is connected to other UCUI devices only via an encrypted wireless network. By design, no data ever leave the system to file servers of third-party service providers. In this way, we ensure the privacy protection of the user.

Advanced methods in NDE using machine learning approaches

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German Industry 4.0.Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive...