Analysis of Verbal and Nonverbal Acoustic Signals with the Dresden UASR System

Hoffmann, Rüdiger; Eichner, Matthias; Wolff, Matthias

doi:10.1007/978-3-540-76442-7_18

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…The development and test of the food intake recognition algorithm and the intake cycle classification algorithm were carried out using the signal recognition tool 'Unified Approach to Speech Synthesis and Recognition (UASR)' of the Institute of Acoustics and Speech Communication at Dresden University of Technology (Hoffmann et al 2007). The framework includes a primary and a secondary feature extraction stage.…”

Section: Food Intake Recognition and Intake Cycle Classificationmentioning

confidence: 99%

Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food

2012

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Obesity and nutrition-related diseases are currently growing challenges for medicine. A precise and timesaving method for food intake monitoring is needed. For this purpose, an approach based on the classification of sounds produced during food intake is presented. Sounds are recorded non-invasively by miniature microphones in the outer ear canal. A database of 51 participants eating seven types of food and consuming one drink has been developed for algorithm development and model training. The database is labeled manually using a protocol with introductions for annotation. The annotation procedure is evaluated using Cohen's kappa coefficient. The food intake activity is detected by the comparison of the signal energy of in-ear sounds to environmental sounds recorded by a reference microphone. Hidden Markov models are used for the recognition of single chew or swallowing events. Intake cycles are modeled as event sequences in finite-state grammars. Classification of consumed food is realized by a finite-state grammar decoder based on the Viterbi algorithm. We achieved a detection accuracy of 83% and a food classification accuracy of 79% on a test set of 10% of all records. Our approach faces the need of monitoring the time and occurrence of eating. With differentiation of consumed food, a first step toward the goal of meal weight estimation is taken.

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Section: Food Intake Recognition and Intake Cycle Classificationmentioning

confidence: 99%

Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food

2012

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“…In our experiments, the UASR [15] system was used for coded speech recognition and synthesis. The phoneme accuracy performance was observed in clean and adverse conditions by usage of various coding-decoding schemes and compression algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…The acoustic model adaptation process uses synthetically generated adaptation data from an existing clean acoustic model. This is provided by the system for unified speech recognition and synthesis (UASR -Unified Automatic Speech Recognition and Synthesis), where common databases and similar algorithms for synthesis and recognition were used [15]. This enables an inverse function of the recognizer system, offering the potential of applying analysis-by-synthesis approach to improve performance of speech recognition systems.…”

Section: Introductionmentioning

confidence: 99%

Analysis-by-synthesis approach for acoustic model adaptation

et al. 2013

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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.

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“…The signal recognition algorithm and the intake cycle recognition algorithm are based on the hierarchical sound analysis model explained in [5]. The signal recognition software tool "Unified Approach to Speech Synthesis and Recognition (UASR)" of the Institute of Acoustics and Speech Communication at Dresden University of Technology is used for feature extraction and classification of the sounds.…”

Section: Algorithm Realizationmentioning

confidence: 99%

Food intake recognition conception for wearable devices

Päßler

Wolff

Fischer

2011

Proceedings of the First ACM MobiHoc Workshop on Pervasive Wireless Healthcare

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Obesity is a growing healthcare challenge in present days. Objective automated methods of food intake monitoring are necessary to face this challenge in future. A method for non-invasive monitoring of human food intake behavior by the evaluation of chewing and swallowing sounds has been developed. A wearable food intake sensor has been created by integrating in-ear microphone and a reference microphone in a hearing aid case. A concept for food intake monitoring requiring low computational cost is presented. After the detection of food intake activity periods, signal recognition algorithms based on Hidden Markov Models distinguish several types of food based on the sound properties of their chewing sounds. Algorithms are developed using manual labeled records of the food intake sounds of 40 participants

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Analysis of Verbal and Nonverbal Acoustic Signals with the Dresden UASR System

Cited by 15 publications

References 12 publications

Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food

Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food

Analysis-by-synthesis approach for acoustic model adaptation

Food intake recognition conception for wearable devices

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