Investigations on the combination of four algorithms to increase the noise robustness of a DSR front-end for real world car data

2005 ◽  
Author(s):  
B. Andrassy ◽  
F. Hilger ◽  
C. Beaugeant
Keyword(s):  
Real World ◽  
Noise Robustness ◽  
Front End

Ethics, Technology Development, and Innovation

1994 ◽  
Vol 4 (3) ◽  
pp. 235-252 ◽  
Author(s):  
Vincent di Norcia
Keyword(s):  
Life Cycle ◽  
Technology Management ◽  
System Design ◽  
Real World ◽  
Technology Development ◽  
Social Economic ◽  
Capital Investments ◽  
Union Carbide ◽  
Front End ◽  
Technology Life Cycle

Abstract:The aim of this essay is to present a model of ethical technology management which assumes that elites who make the system design and development decisions should minimize the risks to stakeholders rather than maximize gains for their organizations. Given the unsettled state in ehical theory a familiar substantive Social, Economic, Environmental and Rights value set or ‘SEER’ ethic is presented. To enable foresight of the negative SEER effects of innovations a technology life cycle is introduced. A cognate issue life cycle is presented to facilitate the ethical resolution of SEER issues associated with such effects. The resultant problem of increased front end load delays and costs, due to ongoing system redesign and stakeholder discussions is found to preferable to high ‘rear end load’ crisis costs, e.g., of the Ford Pinto, Exxon Valdez, Dalkon IUD Shield, and the Union Carbide Bhopal plant. Furthermore the model promises improved returns on the capital investments involved, indications for further research in ethics, economics and organizational theory are noted.“Technology is not preordained. There are choices to be made.”—Ursula Franklin, The Real World of Technology

Psychologically-Inspired Audio-Visual Speech Recognition Using Coarse Speech Recognition and Missing Feature Theory

2017 ◽  
Vol 29 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Kazuhiro Nakadai ◽  
◽  
Tomoaki Koiwa ◽  
Keyword(s):  
Speech Recognition ◽  
Real World ◽  
Visual Speech ◽  
Noise Robustness ◽  
Visual Integration ◽  
The Real ◽  
Visual Speech Recognition ◽  
Missing Feature Theory ◽  
Missing Feature ◽  
Feature Theory

[abstFig src='/00290001/10.jpg' width='300' text='System architecture of AVSR based on missing feature theory and P-V grouping' ] Audio-visual speech recognition (AVSR) is a promising approach to improving the noise robustness of speech recognition in the real world. For AVSR, the auditory and visual units are the phoneme and viseme, respectively. However, these are often misclassified in the real world because of noisy input. To solve this problem, we propose two psychologically-inspired approaches. One is audio-visual integration based on missing feature theory (MFT) to cope with missing or unreliable audio and visual features for recognition. The other is phoneme and viseme grouping based on coarse-to-fine recognition. Preliminary experiments show that these two approaches are effective for audio-visual speech recognition. Integration based on MFT with an appropriate weight improves the recognition performance by −5 dB. This is the case even in a noisy environment, in which most speech recognition systems do not work properly. Phoneme and viseme grouping further improved the AVSR performance, particularly at a low signal-to-noise ratio.**This work is an extension of our publication “Tomoaki Koiwa et al.: Coarse speech recognition by audio-visual integration based on missing feature theory, IROS 2007, pp.1751-1756, 2007.”

scholarly journals Adversarial joint training with self-attention mechanism for robust end-to-end speech recognition

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lujun Li ◽  
Yikai Kang ◽  
Yuchen Shi ◽  
Ludwig Kürzinger ◽  
Tobias Watzel ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Speech Enhancement ◽  
Input Sequence ◽  
Attention Mechanism ◽  
The Self ◽  
Generative Adversarial Networks ◽  
Noise Robustness ◽  
Front End ◽  
Joint Training ◽  
End To End

AbstractLately, the self-attention mechanism has marked a new milestone in the field of automatic speech recognition (ASR). Nevertheless, its performance is susceptible to environmental intrusions as the system predicts the next output symbol depending on the full input sequence and the previous predictions. A popular solution for this problem is adding an independent speech enhancement module as the front-end. Nonetheless, due to being trained separately from the ASR module, the independent enhancement front-end falls into the sub-optimum easily. Besides, the handcrafted loss function of the enhancement module tends to introduce unseen distortions, which even degrade the ASR performance. Inspired by the extensive applications of the generative adversarial networks (GANs) in speech enhancement and ASR tasks, we propose an adversarial joint training framework with the self-attention mechanism to boost the noise robustness of the ASR system. Generally, it consists of a self-attention speech enhancement GAN and a self-attention end-to-end ASR model. There are two advantages which are worth noting in this proposed framework. One is that it benefits from the advancement of both self-attention mechanism and GANs, while the other is that the discriminator of GAN plays the role of the global discriminant network in the stage of the adversarial joint training, which guides the enhancement front-end to capture more compatible structures for the subsequent ASR module and thereby offsets the limitation of the separate training and handcrafted loss functions. With the adversarial joint optimization, the proposed framework is expected to learn more robust representations suitable for the ASR task. We execute systematic experiments on the corpus AISHELL-1, and the experimental results show that on the artificial noisy test set, the proposed framework achieves the relative improvements of 66% compared to the ASR model trained by clean data solely, 35.1% compared to the speech enhancement and ASR scheme without joint training, and 5.3% compared to multi-condition training.

GUERRILLA INNOVATION — THE ACCELERATED RADICAL INNOVATION MODEL MEETS THE REAL WORLD

2012 ◽  
Vol 09 (01) ◽  
pp. 1250002 ◽  
Author(s):  
JOHN A. BERS ◽  
JOHN P. DISMUKES
Keyword(s):  
Real World ◽  
Integrated Approach ◽  
Scaling Up ◽  
Radical Innovation ◽  
Front End ◽  
Innovation Project ◽  
Innovation Model ◽  
External Sources ◽  
Learn Process ◽  
Initial Concept

The Accelerated Radical Innovation (ARI) methodology, an integrated approach to shepherding radical innovation from initial concept through commercialization, was compared to the approach used by an investor-funded seed-stage innovation incubation firm. Similarities include traversal of the same major stages of innovation, emphasis on front-end analysis before escalating commitments, and using an extended "probe-and-learn" process. Key differences were in emphasis. The ARI model relies on analysis and intelligence gleaned from external sources, while ConduIT views each innovation project as unique, requiring tailored responses that will not be found elsewhere. And while the ARI model relies on a structured process, ConduIT favors a more intuitive, people-centered approach. The message for ARI is to become more flexible and adaptable to each innovation's uniquenesses. For ConduIT the challenges are increasing portfolio turnover and scaling up, which will require a more repeatable, teachable, standardized process such as the ARI model.

scholarly journals A Technique for Inspiring Scientific Inquiry Using a Creative Scenario

2017 ◽  
Vol 79 (8) ◽  
pp. 671-677 ◽  
Author(s):  
Cassandra M. Berry
Keyword(s):  
Real World ◽  
Scientific Inquiry ◽  
Interdisciplinary Collaboration ◽  
Testable Hypothesis ◽  
Learning Approach ◽  
Data Sets ◽  
Front End ◽  
Design Students ◽  
Creative Problem ◽  
Real World Problems

Teaching scientific inquiry in large interdisciplinary classes is a challenge. We describe a creative problem-based learning approach, using a motivational island crisis scenario, to inspire research design. Students were empowered to formulate their individual scientific inquiry and then guided to develop a testable hypothesis, aims, and objectives in designing a research proposal. Personalized data sets matched to the research objectives were provided to individual students for analysis and presentation. This technique helps students to gain critical insights into the global value of interdisciplinary collaboration toward solving complex real-world problems. Students learn the front end of research, how to formulate a line of scientific inquiry and design an innovative research project—both important skills for them as tomorrow's leaders and entrepreneurs.

Replications can cause distorted belief in scientific progress

2018 ◽  
Vol 41 ◽  
Author(s):  
Michał Białek
Keyword(s):  
Real World ◽  
Scientific Progress ◽  
The Public ◽  
Psychological Science

AbstractIf we want psychological science to have a meaningful real-world impact, it has to be trusted by the public. Scientific progress is noisy; accordingly, replications sometimes fail even for true findings. We need to communicate the acceptability of uncertainty to the public and our peers, to prevent psychology from being perceived as having nothing to say about reality.

Current Issues: Evidence-Based Practice for the Real World: Thoughts on Clinical Education in Communication Disorders

2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe
Keyword(s):  
Clinical Education ◽  
Real World ◽  
Evidence Based Practice ◽  
Communication Disorders ◽  
University Faculty ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Basic Principles ◽  
The Real ◽  
Language Pathology

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.

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