Two PhD student positions in the field of speech signal processing and machine learning for pathological speech enhancement. F/H

About Idiap

Idiap is an independent, not-for-profit, research institute accredited and funded by the Swiss Federal Government, the State of Valais, and the City of Martigny.

Idiap offers competitive salaries and working conditions at all levels in a dynamic, multicultural environment. Idiap is an equal opportunity employer. We specifically encourage women and minorities to apply.

Idiap is located in the town of Martigny in Valais, Switzerland, offering exceptional quality of life, exciting recreational activities, including hiking, climbing and skiing, as well as varied cultural activities. It is within close proximity to Lausanne and Geneva. Although Idiap is located in the French part of Switzerland, English is the official working language.

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Job description

The Idiap Research Institute seeks qualified candidates for two PhD student positions in the field of speech signal processing and machine learning for pathological speech enhancement.

Digital communication plays a vital role in our daily lives, occurring in many different forms including mobile communication, meeting through teleconferencing platforms such as Zoom, and using voice-activated agents such as Apple’s Siri or Amazon’s Alexa. Since we live in a noisy world, the recorded microphone signals in these digital communication applications are often contaminated by background noise. Background noise causes signal degradation, thereby impairing speech quality and intelligibility and decreasing the performance of many signal processing techniques required in several applications for a high fidelity communication experience. To deal with background noise, speech enhancement approaches aiming to recover the clean speech signal are indispensable. As such, a wide range of speech enhancement approaches have been proposed in past decades, with e.g., the traditional statistical model-based approaches and the more recent deep learning-based approaches having shown strong enhancement performance. Although these approaches have shown advantageous enhancement performance, they have been devised for scenarios where the target speakers are neurotypical speakers, i.e., speakers that do not exhibit any speech impairments. However, many pathological conditions such as hearing loss, head and neck cancers, or neurological disorders, disrupt the speech production mechanism, resulting in speech impairments across different dimensions. Preliminary investigations show that state-of-the-art enhancement approaches can yield a considerably lower performance for pathological signals than for neurotypical signals. Although conditions resulting in pathological speech are widely prevalent, speech enhancement approaches specifically targeting pathological speech have never been established. The successful PhD candidate will develop model-based and deep learning-based speech enhancement approaches that yield an advantageous performance for pathological speech.

The successful PhD candidate will join the Signal Processing for Communication group at Idiap, under the supervision of Dr. Ina Kodrasi. They will also become a doctoral student at EPFL (http://www.epfl.ch) conditional on parallel application to, and acceptance by, the EPFL Doctoral School (http://phd.epfl.ch/applicants). Appointment for the PhD position is for a maximum of 4 years, provided successful progress, and should lead to a dissertation. Starting date is to be negotiated. All queries related to the advertised position can be sent to Dr. Ina Kodrasi (ina.kodrasi@idiap.ch).

At Idiap we place great emphasis on diversity and strongly encourage individuals from underrepresented groups to apply.

Sought profile

For PhD's
The ideal PhD candidate should hold a Masters degree in electrical engineering, computer science, or related fields. They should have a background in statistics, linear algebra, signal processing, and machine learning. The applicant should also have strong programming skills.

In order to avoid discrimination, please do not mention your gender, age and marital status on your CV.

Langues souhaitées

• Anglais - Niveau avancé

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