Abstract
This paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users.
Highlights
In order to contribute to the development of a community, we should take into account its socio-cultural and economic context
This paper proposes a multilingual audio information management system based on semantic knowledge in complex environments
We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks
Summary
In order to contribute to the development of a community, we should take into account its socio-cultural and economic context. We propose a joint solution to the socio-cultural context of the Basque Country and the needs and characteristics of small and medium companies with limited staff and economic resources, which are one of the foundations of the economy of the area. Innovative technologies have to be adequately designed so that they suit the needs of the industrial fabric, and they can be integrated into real applications. Those technologies should be designed including auto-changing and autolearning abilities. These capabilities will allow systems to automatically learn from new data and to adapt its components to new conditions, increasing system robustness [1]
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