Dissemination: ASLABI
On Thursday, October 27, 2022, we presented the ReSSInt project at the headquarters of ASLABI, the Biscay Association of Laryngectomized Patients,in Bilbao. We had a friendly meeting with the associates and we answered the questions they asked us. We also explain what the recording process…
Open position: Programa Investigo
The proposed work consists of investigating strategies to reduce computational needs and allow the integration of synthetic neural voices in mobile devices. We are looking for a person with programming skills, who must be registered with Lanbide. El trabajo propuesto consiste en la investigación…
Dissemination: Secretos de las Telecomunicaciones
Framed into the European Researchers' Night Initiative, part of the work from Aholab will be presented in the event Secretos de las telecomunicaciones, Telekomunikazioen sekretuak, on Friday September 30th.
Open position
We have a vacancy in the project, in order to work on voice generation from muscle activity signals. We are seeking a candidate skilled in communications, signal processing or machine learning. If you think you suit the position, and feel like working with us, please find the relevant…
Dissemination: Open Door Days
Presentation of Aholab's work to high school students within the Open Door Days of the University of the Basque Country. 2022ReSSint_PPAA.final_.EU_Download
PhD Thesis Presentation: Oesophageal speech:enrichment and evaluations
Student: Sneha Raman Supervisor: Eva Navas Cordón and Inma Hernáez Rioja Date: 22-12-2021 Description: The aim of this work is to use a voice conversion methodology that would enrich alaryngeal speech by improving its intelligibility and maintaining speaker identity. We also propose to…
Participate on Ressint
You can take part in Ressint. Find all the information attached: Reclutar_ressint_on-1Download
Master Thesis Presentation: Direct speech synthesis from EMG data
Student: Xabier Román Advisor: Eva Navas Presented: 05/10/21 Silent speech interfaces allow the generation of acoustic speech from articulatory data obtained from some sensors. In this master thesis we propose to develop a Neural Network based system able to produce speech from Electromiographic…