Multimodal Diarization Systems by Training Enrollment Models as Identity Representations

This paper describes a post-evaluation analysis of the system developed by ViVoLAB research group for the IberSPEECH-RTVE 2020 Multimodal Diarization (MD) Challenge. This challenge focuses on the study of multimodal systems for the diarization of audiovisual files and the assignment of an identity to each segment where a person is detected. In this work, we implemented two different subsystems to address this task using the audio and the video from audiovisual files separately. To develop our subsystems, we used the state-of-the-art speaker and face verification embeddings extracted from publicly available deep neural networks (DNN). Different clustering techniques were also employed in combination with the tracking and identity assignment process. Furthermore, we included a novel back-end approach in the face verification subsystem to train an enrollment model for each identity, which we have previously shown to improve the results compared to the average of the enrollment data. Using this approach, we trained a learnable vector to represent each enrollment character. The loss function employed to train this vector was an approximated version of the detection cost function (aDCF) which is inspired by the DCF widely used metric to measure performance in verification tasks. In this paper, we also focused on exploring and analyzing the effect of training this vector with several configurations of this objective loss function. This analysis allows us to assess the impact of the configuration parameters of the loss in the amount and type of errors produced by the system

Direct Text to Speech Translation System using Acoustic Units

This paper proposes a direct text to speech translation system using discrete acoustic units. This framework employs text in different source languages as input to generate speech in the target language without the need for text transcriptions in this language. Motivated by the success of acoustic units in previous works for direct speech to speech translation systems, we use the same pipeline to extract the acoustic units using a speech encoder combined with a clustering algorithm. Once units are obtained, an encoder-decoder architecture is trained to predict them. Then a vocoder generates speech from units. Our approach for direct text to speech translation was tested on the new CVSS corpus with two different text mBART models employed as initialisation. The systems presented report competitive performance for most of the language pairs evaluated. Besides, results show a remarkable improvement when initialising our proposed architecture with a model pre-trained with more languages.Comment: 5 pages, 4 figure

Improved Cross-Lingual Transfer Learning For Automatic Speech Translation

Research in multilingual speech-to-text translation is topical. Having a single model that supports multiple translation tasks is desirable. The goal of this work it to improve cross-lingual transfer learning in multilingual speech-to-text translation via semantic knowledge distillation. We show that by initializing the encoder of the encoder-decoder sequence-to-sequence translation model with SAMU-XLS-R, a multilingual speech transformer encoder trained using multi-modal (speech-text) semantic knowledge distillation, we achieve significantly better cross-lingual task knowledge transfer than the baseline XLS-R, a multilingual speech transformer encoder trained via self-supervised learning. We demonstrate the effectiveness of our approach on two popular datasets, namely, CoVoST-2 and Europarl. On the 21 translation tasks of the CoVoST-2 benchmark, we achieve an average improvement of 12.8 BLEU points over the baselines. In the zero-shot translation scenario, we achieve an average gain of 18.8 and 11.9 average BLEU points on unseen medium and low-resource languages. We make similar observations on Europarl speech translation benchmark

Estudio y desarrollo de técnicas no supervisadas para la extracción automática de características en datos multimedia

El objetivo de este Trabajo Fin de Máster es el desarrollo de sistemas automáticos basados en redes neuronales profundas mediante técnicas no supervisadas innovadoras para realizar la extracción de vectores de características representativos sin la utilización de datos etiquetados. Los sistemas resultantes consiguen una mayor capacidad de representación de datos de forma generalizable, permitiendo su aplicación a diferentes tareas con datos de naturaleza muy distinta. Los sistemas se han desarrollado mediante infraestructuras de cálculo masivo distribuido, centrando su aplicación en el tratamiento de datos de vídeo. El trabajo se enmarca en la línea de investigación de aprendizaje automático sobre datos multimedia del Voice Input Voice Output Laboratory (ViVoLab) del Instituto de Investigación en Ingeniería de Aragón (I3A) de la Universidad de Zaragoza.Este trabajo se ha realizado a partir de dos bases de datos de vídeo de diferente tamaño. La primera de ellas se utiliza para el desarrollo de modelos no supervisados, mientras que la segunda se aplica en la evaluación de sus prestaciones en dos tareas secundarias que demuestran su versatilidad y potencia. Para desarrollar estos modelos se han utilizado diferentes arquitecturas, como redes neuronales convolucionales, transformers y redes neuronales recurrentes. Además, al trabajar con señales multimedia, su análisis mediante modelos de secuencia mejora de las capacidades de extracción de información relevante.La primera de las tareas secundarias mencionadas consiste en un sistema de reconocimiento de acciones mediante la clasificación de un conjunto reducido de vídeos etiquetados. En primer lugar, se construyen varios sistemas supervisados de referencia. Posteriormente, se construye un extractor de características mediante aprendizaje no supervisado. El sistema resultante aprende a representar los datos en un espacio de características potente de forma automática sin etiquetar ningún dato de entrada. Este extractor se aplica a la tarea de clasificación y se comprueba su potencia mediante la comparativa con la referencia supervisada.En la segunda tarea secundaria se aplican directamente los modelos no supervisados entrenados para la construcción de una herramienta de motor de búsqueda en la que se obtiene, de forma automática a partir de la representación obtenida, un conjunto de vídeos similares a un vídeo de entrada desde el punto de vista de su contenido y temática.<br /

The patient with depression at work

Originales[ES] La distinción entre sentimientos de tristeza normal, síndrome de desgaste profesional y los trastornos depresivos propiamente dichos, no siempre es fácil. En demasiadas ocasiones se carece de protocolos de actuación adecuados y las propias condiciones laborales impiden un abordaje satisfactorio e integral; es más, dichas condiciones pueden incluso actuar como desencadenantes, mantenedores o mediadores de muchos de estos problemas. La depresión es una enfermedad como otra cualquiera y que nos puede pasar a cualquiera, pero en más de la mitad de los casos tiende a cronificarse y/o complicarse con otras enfermedades. Las consecuencias serán muy negativas, tanto en los trabajadores como en las organizaciones, si no se diagnostica de forma precoz y no se trata de forma efectiva. Con este objetivo, se revisa cada subtipo de trastorno, sus posibles efectos y reconocimiento en el trabajo, el abordaje de la persona afectada y el tratamiento.[EN] The distinction among normal feelings of sadness, burnout and depressive disorders is not always easy to differentiate. In too many occasions, we lack the accurate procedures to deal with these problems and very often work conditions are an impediment to carry out a satisfactory and complete approach. These conditions can even originate, keep or mediate in many of the cases. Depression is a disease as any other and anyone can have it, but more of the 50% of the cases tend to cronify or complicate with other diseases. If we don’t do an early diagnosis to treat it in an effective way, the consequences will be very negative for workers and organizations. With this purpose we review each type of depressive disorder, its possible effects and recognition at work to approach an treat the ill person.N

Workers with mental health problems. General guidelines for detection, intervention and prevention

Actualizaciones y revisiones[ES] Numerosos estudios han demostrado las relaciones entre condiciones psicosociales del trabajo y la salud mental de los empleados, y especialmente cómo la combinación de bajo control percibido y altas demandas laborales predicen determinados problemas de salud mental. Trastornos mentales tales como los de ansiedad y depresión tienen un efecto muy negativo sobre la calidad de vida y la capacidad funcional en el trabajo. Además, la salud mental del trabajador puede afectar a la percepción de las características del trabajo. Como consecuencia, es fundamental diseñar trabajos saludables, programas de reducción de estrés para los empleados y para la reincorporación laboral de empleados con trastornos mentales. El objetivo del presente artículo es revisar las principales líneas de prevención, detección e intervención en las organizaciones, que pueden promover políticas saludables para la atención e integración de los trabajadores con trastorno mental. [EN] Numerous studies have demonstrated the relationship between psychosocial work conditions and the mental health of employees, and especially the combination of low perceived control and high labor demands predicts mental health problems. Mental health disorders such us anxiety or depression have a big detrimental effect on the individual’s quality of life and the ability to function in the workplace. Mental health may also affect the perception of work characteristics. As a consequence, it is essential to design healthy jobs, stress reduction programs and return to work programs for employees with mental disorders. The aim of this paper is to review the main lines of prevention, detection and intervention in organizations that can promote healthy policies for the care and integration of workers with mental disorder.N

Depression in internal medicine inpatients at the time of hospital discharge and referral to primary care

Background and objectives: This is the first multi-center study intended to document the prevalence, characteristics, and associations of depression in Medicine patients at the time of hospital discharge and their referral to Primary Care (PC). Methods: Adult patients randomly selected among consecutive admissions to Medicine wards in 8 hospitals in Spain, covering health districts, were examined in a two-phase ''case-finding'' procedure. Standardized, Spanish versions of instruments were used, including the Standardized Polyvalent Psychiatric Interview (SPPI) and Cumulative Illness Rating Scale (CIRS). Cases of depression were diagnosed according to ICD-10 general hospital research criteria. Results: Three hundred and twelve patients with treatable depression and 777 non-depressed controls were identified. In a conservative estimate, the global prevalence of major depression was 7.1%, dysthymia 4.2% and adjustment depression 7.1%, and 51.9% of cases were of moderate/ severe intensity. Depression was more frequent in women, the differences being significant in all categories of depression. The prevalence of depression was lower in individuals aged 85 or more years, the differences being significant in cases of both dysthymia and adjustment depression. A clear pattern of decreasing prevalence with age was observed in women. The depressed had as an average five medical systems affected, and higher CIRS scores compared with the controls, the differences being significant in cases of both major depression and dysthymia. Conclusions: This is the first report showing a considerable prevalence of treatable cases of depression in Medicine patients at the time of hospital discharge and referral to PC. Depression is associated with the severity of the medical condition, and differences observed by age and sex have clinical implications. Paper read at the 3rd Annual Meeting of the European Association of Psychosomatic Medicine, Nuremberg 2015. © 2022 Asociación Universitaria de Zaragoza para el Progreso de la Psiquiatría y la Salud Menta

Small renal masses in Latin-American population : Characteristics and prognostic factors for survival, recurrence and metastasis - A multi-institutional study from LARCG database

To evaluate demographic, clinical and pathological characteristics of small renal masses (SRM) (≤ 4 cm) in a Latin-American population provided by LARCG (Latin-American Renal Cancer Group) and analyze predictors of survival, recurrence and metastasis. A multi-institutional retrospective cohort study of 1523 patients submitted to surgical treatment for non-metastatic SRM from 1979 to 2016. Comparisons between radical (RN) or partial nephrectomy (PN) and young or elderly patients were performed. Kaplan-Meier curves and log-rank tests estimated 10-year overall survival. Predictors of local recurrence or metastasis were analyzed by a multivariable logistic regression model. PN and RN were performed in 897 (66%) and 461 (34%) patients. A proportional increase of PN cases from 48.5% (1979-2009) to 75% (after 2009) was evidenced. Stratifying by age, elderly patients (≥ 65 years) had better 10-year OS rates when submitted to PN (83.5%), than RN (54.5%), p = 0.044. This disparity was not evidenced in younger patients. On multivariable model, bilaterality, extracapsular extension and ASA (American Society of Anesthesiologists) classification ≥3 were predictors of local recurrence. We did not identify significant predictors for distant metastasis in our series. PN is performed in Latin-America in a similar proportion to developed areas and it has been increasing in the last years. Even in elderly individuals, if good functional status, sufficiently fit to surgery, and favorable tumor characteristics, they should be encouraged to perform PN. Intending to an earlier diagnosis of recurrence or distant metastasis, SRM cases with unfavorable characteristics should have a more rigorous follow-up routine

Preventive usefulness of the concept “serious mental disorder” in social-sanitary area

Artículos originales[ES] Introducción: El constructo “trastorno mental grave” (TMG) puede ser muy útil en la práctica clínica y no sólo en el ámbito de la gestión de los recursos, al permitir diseñar intervenciones preventivas, diagnósticas y terapéuticas efectivas, especialmente en el sector servicios y en profesiones de alta fiabilidad y seguridad (sanidad, defensa, transporte, etc.) en las que los errores suelen tener consecuencias directas adversas para la vida de las personas y los que se debe garantizar la máxima calidad y la seguridad de los usuarios. Objetivos: Revisar los principales aspectos a considerar en la elaboración de protocolos de intervención laboral ante trastornos mentales graves. Método: Revisión de la bibliografía disponible. Conclusiones: Se proponen herramientas y procedimientos de utilidad para afrontar los casos clínicos graves en el ámbito laboral sanitario, mejorar la calidad asistencial y contribuir a garantizar la seguridad de los usuarios y de los sanitarios enfermos. [EN] Introduction: The concept of “serious mental disorder” (SMD) can be very useful in clinical medical practice, not only in the management resources area, allowing to design preventive, diagnostic and therapeutic effective interventions, but also in service area and in high reliability and security professions, such as health, defence, transport and so on. In these cases mistakes use to have direct and adverse consequences for people’s life that is way a maximum quality and safety must be guaranteed for the users. Objective: To check the principal aspects to consider in the production of intervention protocols for mental serious disorders in the workplace. Method: Review of the available bibliography. Conclusions: This article proposes tools and procedures to deal with serious clinical cases in health professional field in order to improve the attendance quality, contributing to the safety of the health workers and users.N

Utilidad preventiva del constructo "trastorno mental grave" en el ámbito sociosanitario Preventive usefulness of the concept "serious mental disorder" in social-sanitary area

Introducción: El constructo "trastorno mental grave" (TMG) puede ser muy útil en la práctica clínica y no sólo en el ámbito de la gestión de los recursos, al permitir diseñar intervenciones preventivas, diagnósticas y terapéuticas efectivas, especialmente en el sector servicios y en profesiones de alta fiabilidad y seguridad (sanidad, defensa, transporte, etc.) en las que los errores suelen tener consecuencias directas adversas para la vida de las personas y los que se debe garantizar la máxima calidad y la seguridad de los usuarios. Objetivos: Revisar los principales aspectos a considerar en la elaboración de protocolos de intervención laboral ante trastornos mentales graves. Método: Revisión de la bibliografía disponible. Conclusiones: Se proponen herramientas y procedimientos de utilidad para afrontar los casos clínicos graves en el ámbito laboral sanitario, mejorar la calidad asistencial y contribuir a garantizar la seguridad de los usuarios y de los sanitarios enfermos.Introduction: The concept of "serious mental disorder" (SMD) can be very useful in clinical medical practice, not only in the management resources area, allowing to design preventive, diagnostic and therapeutic effective interventions, but also in service area and in high reliability and security professions, such as health, defence, transport and so on. In these cases mistakes use to have direct and adverse consequences for people's life that is way a maximum quality and safety must be guaranteed for the users. Objective: To check the principal aspects to consider in the production of intervention protocols for mental serious disorders in the workplace. Method: Review of the available bibliography. Conclusions: This article proposes tools and procedures to deal with serious clinical cases in health professional field in order to improve the attendance quality, contributing to the safety of the health workers and users