How to Do Things With Words

Publisher Summary Environmental noise can affect acoustic communication through limiting the broadcast area, or active space, of a signal by decreasing signal-to-noise ratios at the position of the receiver. At the same time, noise is ubiquitous in all habitats and is, therefore, likely to disturb animals, as well as humans, under many circumstances. However, both animals and humans have evolved diverse solutions to the background noise problem, and this chapter reviews recent advancements in studies of vocal adaptations to interference by background noise and relate these to fundamental issues in sound perception. The chapter starts with the discussion of sender's side by considering potential evolutionary shaping of species-specific signal characteristics and individual short‐term adjustments of signal features. Subsequently, it focuses on the receivers of signals and reviews their sensory capacities for signal detection, recognition, and discrimination and relates these issues to auditory scene analysis and the ecological concept of signal space. The data from studies on insects, anurans, birds, and mammals, including humans, and to a lesser extent available work on fish and reptiles is also discussed in the chapter.

Acoustic Communication in Noise

The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas. In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or transducers.

Grammatical Inference: Learning Automata and Grammars

Process mining in healthcare

Studies have shown that variability introduced by stress or emotion can severely reduce speech recognition accuracy. Techniques for detecting or assessing the presence of stress could help improve the robustness of speech recognition systems. Although some acoustic variables derived from linear speech production theory have been investigated as indicators of stress, they are not always consistent. Three new features derived from the nonlinear Teager (1980) energy operator (TEO) are investigated for stress classification. It is believed that the TEO based features are better able to reflect the nonlinear airflow structure of speech production under adverse stressful conditions. The features proposed include TEO-decomposed FM variation (TEO-FM-Var), normalized TEO autocorrelation envelope area (TEO-Auto-Env), and critical band based TEO autocorrelation envelope area (TEO-CB-Auto-Env). The proposed features are evaluated for the task of stress classification using simulated and actual stressed speech and it is shown that the TEO-CB-Auto-Env feature outperforms traditional pitch and mel-frequency cepstrum coefficients (MFCC) substantially. Performance for TEO based features are maintained in both text-dependent and text-independent models, while performance of traditional features degrades in text-independent models. Overall neutral versus stress classification rates are also shown to be more consistent across different stress styles.

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Nonlinear feature based classification of speech under stress

Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models

The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.

/pdf/process-mining-methodology-for-health-process-tracking-using-h7589nvfic.pdf

Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems.

In the framework of the DIHANA project, we present the acquisitionprocess of a spontaneous speech dialogue corpus in Spanish. Theselected application consists of information retrieval by telephone for nationwide trains. A total of 900 dialogues from 225 users were acquired using the Wizard of Oz technique. In this work, we present the design and planning of the dialogue scenes and the wizard strategy used for the acquisition of the corpus. Then, we also present the acquisition tools and a description of the acquisition process.

/pdf/design-and-acquisition-of-a-telephone-spontaneous-speech-1nhvzbhs8w.pdf

Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA.

The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection.

https://www.mdpi.com/1424-8220/13/11/15434/pdf

José-Miguel Benedí

Papers

Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models

Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems.

Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA.

Process mining for individualized behavior modeling using wireless tracking in nursing homes.

An analysis of general acoustic-phonetic features for Spanish speech produced with the Lombard effect