Similarity Measure Optimization for Target Detection

Author(s):  
Batuhan Gundogdu ◽  
Murat Saraclar

Today, in the era of data and computing, fast and reliable retrieval of information has become of great importance for security and military applications, and continues to be such, as the amount available digital data increases every second. While the search and retrieval of text data has produced mature products and are today being used in search engines everyday by everyone, the retrieval of spoken content still remains a young research, especially for low resource languages where the available data is scarce to train reliable speech recognition systems. This chapter provides a thorough introduction of a speech retrieval task called “keyword search” and presents a novel similarity measure optimization-based approach. The case study was experimented on telephone conversations in three different languages and thousands of keywords randomly selected from each language were searched in the document. The experiments show that the technique introduced in this chapter offers a new methodology to handle the terms that does not even exist in the vocabulary of the speech recognition systems.

2021 ◽  
Vol 11 (19) ◽  
pp. 8872
Author(s):  
Iván G. Torre ◽  
Mónica Romero ◽  
Aitor Álvarez

Automatic speech recognition in patients with aphasia is a challenging task for which studies have been published in a few languages. Reasonably, the systems reported in the literature within this field show significantly lower performance than those focused on transcribing non-pathological clean speech. It is mainly due to the difficulty of recognizing a more unintelligible voice, as well as due to the scarcity of annotated aphasic data. This work is mainly focused on applying novel semi-supervised learning methods to the AphasiaBank dataset in order to deal with these two major issues, reporting improvements for the English language and providing the first benchmark for the Spanish language for which less than one hour of transcribed aphasic speech was used for training. In addition, the influence of reinforcing the training and decoding processes with out-of-domain acoustic and text data is described by using different strategies and configurations to fine-tune the hyperparameters and the final recognition systems. The interesting results obtained encourage extending this technological approach to other languages and scenarios where the scarcity of annotated data to train recognition models is a challenging reality.


2000 ◽  
Vol 16 (3) ◽  
pp. 186-196 ◽  
Author(s):  
Karen Hux ◽  
Joan Rankin-Erickson ◽  
Nancy Manasse ◽  
Elizabeth Lauritzen

Author(s):  
Conrad Bernath ◽  
Aitor Alvarez ◽  
Haritz Arzelus ◽  
Carlos David Martínez

Author(s):  
Sheng Li ◽  
Dabre Raj ◽  
Xugang Lu ◽  
Peng Shen ◽  
Tatsuya Kawahara ◽  
...  

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 33
Author(s):  
Olena Klymenko ◽  
Lise Lillebrygfjeld Halse ◽  
Bjørn Jæger

Sustainability accounting is an emerging research area receiving growing awareness. This study examines the role of digital technology in manufacturing companies’ sustainability accounting. To guide the research, we use a triple layered business model canvas, which supports the accounting of a manufacturer’s performance for the economic, environmental, and social aspects of sustainability. We present an explorative case study of four Norwegian manufacturing companies representing different industries. The findings from the study indicate that while accounting for economic values is well taken care of, companies do not perform comprehensive environmental and social accounting. Furthermore, we observed a shift from a focus on sustainability issues related to the internal manufacturing process to a focus on sustainability issues for the life cycle of the product. Even though the manufacturers are at the forefront with regard to automation and control of production, with extensive use of robots giving a large amount of data, these data are not utilized towards sustainability accounting, showing that sustainability and digitalization are seen as two separate phenomena. This study sheds light on how digital data available from applied Industry 4.0 technologies could enhance sustainability accounting with limited efforts, linking sustainability and digitalization. The results provide insights for manufacturers and researchers in moving towards more sustainable operations and products.


Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 130-135
Author(s):  
Christian Deuerlein ◽  
Moritz Langer ◽  
Julian Seßner ◽  
Peter Heß ◽  
Jörg Franke

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