Multilingual audio information management system based on semantic knowledge in complex environments

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.This work is being funded by Grants: TEC201677791-C4 from Plan Nacional de I + D + i, Ministry of Economic Affairs and Competitiveness of Spain and from the DomusVi Foundation Kms para recorder, the Basque Government (ELKARTEK KK-2018/00114, GEJ IT1189-19, the Government of Gipuzkoa (DG18/14 DG17/16), UPV/EHU (GIU19/090), COST ACTION (CA18106, CA15225)

Selección de unidades léxicas para reconocimiento antomático del habla continua en euskera

El euskera es una lengua aglutinante, lo que implica que el vocabulario de un corpus no puede definirse mediante palabras porque crece combinatorialmente y se hace intratable para tareas de medio y gran vocabulario. Los seudo-morfemas, generados mediante una herramienta de segmentación automatizada pueden ser una buena alternativa para la construcción del lexicón y de un modelo del lenguaje, puesto que reducen notablemente el tamaño del vocabulario. En euskera el número de morfemas cortos y acústicamente muy parecidos es muy alto. Este es un fenómeno que debe de tenerse en cuenta ya que el proceso de decodificación acústico fonética puede influir en el CSR, al aumentar la posibilidad de confusión e inserción de ciertas unidades léxicas (unidades muy cortas y con alta tasa de confusión acústica). Una posible forma de abordar el problema es no segmentar estas unidades. El siguiente paso en la mejora del sistema de CSR en euskera es la utilización de un modelo de lenguaje para guiar el proceso de reconocimiento.Basque is an agglutinative language, which implies that corpus vocabulary can not be defined with words, because they grow combinationally making medium and large vocabulary tasks intractable. Pseudo-morphemes, generated with an automatic segmentation tool, could be an alternative choice for building the lexicon and the language model, for they notably reduce the vocabulary size. In Basque, there are many short and acoustically very similar morphemes. This phenomenon has to be taken into account, because the acoustic-phonetic decodification process can influence the CSR task, increasing the possibility of confusion and insertion of certain lexical units (very short units with high rates of acoustic confusion). A feasible way to deal with this problem is to avoid the segmentation of those units. The next step to improve the CSR system in Basque is the use of a language model in order to guide the recognition process

Design of Three Electric Vehicle Charging Tariff Systems to Improve Photovoltaic Self-Consumption

Electric vehicles (EVs) are emerging as one of the pillars for achieving climate neutrality. They represent both a threat and an opportunity for the operation of the network. Used as flexible loads, they can favor the self-consumption of photovoltaic (PV) energy. This paper presents three EV charging tariff systems (TSs) based on the self-consumption of excess PV energy. The TS objectives are to increase the self-consumption rate (SCR) and thus indirectly decrease the charging cost of the EV users. Two of the proposed TSs correspond to an indirect control of EV charging. The third TS is a hybrid system where the charging power is controlled. The TS is designed using a series of rules that consider the momentary PV surplus and the charging power of each EV. The influence of the TS is simulated by considering real data from a PV collective self-consumption project in the Basque Country (Spain). The TS simulations performed with 6 months of data show a 13.1% increase in the SCR when applying the third TS, reaching an average of 93.09% for the SCR. In addition, the cost of EV charging is reduced by 25%

Selección de unidades léxicas para reconocimiento antomático del habla continua en euskera

Basque is an agglutinative language, which implies that corpus vocabulary can not be defined with words, because they grow combinationally making medium and large vocabulary tasks intractable. Pseudo-morphemes, generated with an automatic segmentation tool, could be an alternative choice for building the lexicon and the language model, for they notably reduce the vocabulary size. In Basque, there are many short and acoustically very similar morphemes. This phenomenon has to be taken into account, because the acoustic-phonetic decodification process can influence the CSR task, increasing the possibility of confusion and insertion of certain lexical units (very short units with high rates of acoustic confusion). A feasible way to deal with this problem is to avoid the segmentation of those units. The next step to improve the CSR system in Basque is the use of a language model in order to guide the recognition process.El euskera es una lengua aglutinante, lo que implica que el vocabulario de un corpus no puede definirse mediante palabras porque crece combinatorialmente y se hace intratable para tareas de medio y gran vocabulario. Los seudo-morfemas, generados mediante una herramienta de segmentación automatizada pueden ser una buena alternativa para la construcción del lexicón y de un modelo del lenguaje, puesto que reducen notablemente el tamaño del vocabulario. En euskera el número de morfemas cortos y acústicamente muy parecidos es muy alto. Este es un fenómeno que debe de tenerse en cuenta ya que el proceso de decodificación acústico fonética puede influir en el CSR, al aumentar la posibilidad de confusión e inserción de ciertas unidades léxicas (unidades muy cortas y con alta Osos de confusión acústica). Una posible forma de abordar el problema es no segmentar estas unidades. El siguiente paso en la mejora del sistema de CSR en euskera es la utilización de un modelo de lenguaje para guiar el proceso de reconocimiento. El euskera es una lengua aglutinante, lo que implica que el vocabulario de un corpus no puede definirse mediante palabras porque crece combinatorialmente y se hace intratable para tareas de medio y gran vocabulario. Los seudo-morfemas, generados mediante una herramienta de segmentación automatizada pueden ser una buena alternativa para la construcción del lexicón y de un modelo del lenguaje, puesto que reducen notablemente el tamaño del vocabulario. En euskera el número de morfemas cortos y acústicamente muy parecidos es muy alto. Este es un fenómeno que debe de tenerse en cuenta ya que el proceso de decodificación acústico fonética puede influir en el CSR, al aumentar la posibilidad de confusión e inserción de ciertas unidades léxicas (unidades muy cortas y con alta Osos de confusión acústica). Una posible forma de abordar el problema es no segmentar estas unidades. El siguiente paso en la mejora del sistema de CSR en euskera es la utilización de un modelo de lenguaje para guiar el proceso de reconocimiento

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer's disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.Peer ReviewedPostprint (published version

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer's disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.Peer Reviewe