Research Assistant (Master Thesis Worker): Machine Learning -Based Prediction Of Speech Signal’S Sound Pressure Level

The Department of Information and Communications Engineering at Aalto University carries out research and education in signals, machine learning, speech and audio technology, and human-centered information processing. In Professor Paavo Alku's research group, we develop methods and models for robust speech technologies, including applications in speech-based health assessment.

We are now looking for a paid

Research Assistant as master's thesis worker in Sound Pressure Level (SPL) Regression from Speech.

Are you excited about biomarking of human health based on acoustic cues embedded in speech signals and want your thesis to enable practical assessment from everyday speech recordings?

We are looking for a master’s thesis worker to study regression-based prediction of the sound pressure level (SPL; vocal intensity) of speech. SPL is conventionally measured with a sound level meter or by using calibrated recordings, but these procedures are not available in many real-life scenarios (e.g., mobile apps or recordings transmitted over phone calls). Recent works by Alku et al. 2024 (Speech Comm.) and Kodali et al. (JASA 2025) demonstrated that SPL can be estimated from non-calibrated speech recordings using machine learning. In this thesis, you will build on that line of work and extend it towards real-world deployment conditions.

In order to develop speech-based health technology, a key challenge is robustness: real-life recordings vary across devices, microphones, mouth-to-microphone distances, rooms, background noise, and speech codecs (including telephony). These factors introduce channel distortions and level variability that can degrade SPL prediction.

The goal is to develop and evaluate SPL prediction pipelines that generalize across such conditions. Depending on your interests, the work can include signal-processing-based feature design, modelling and regression, calibration strategies, and evaluation with recordings collected in realistic environments.

Your role and goals

In this position, you will design and run experiments for SPL prediction from speech, with an emphasis on robust signal processing and careful evaluation under realistic recording conditions. The work is research-oriented and hands-on, and will contribute to methods developed in the group. Typical tasks include:

Your team

You will work in Professor Paavo Alku's research group at Aalto University. Your thesis supervisor will be Professor Paavo Alku, and your day-to-day advisor will be Saska Tirronen. You will also collaborate with other researchers in the group and benefit from an active research environment.

Your experience and ambitions

We are looking for a candidate who is motivated to learn and to build reliable experimental pipelines. The ideal candidate has:

Language requirements:
English: Working proficiency in English is required.
Finnish: Finnish language is not required. Finnish proficiency is considered an advantage.

If you are looking for a thesis at the intersection of speech and language technology, acoustics, and practical machine learning, this position offers a clear research question with real-world relevance and strong academic supervision.

What we offer
We offer a supportive environment for completing a high-quality thesis and building practical research skills. In this role, you will benefit from:

Start date is as soon as possible. Working arrangements are flexible and will be agreed based on your thesis schedule.

Join us!
Please submit your application as a single PDF file by 15.3.2026 through our recruitment system using the “Apply now!” link below.

Your application should include:

The position is primarily open to Aalto University M.Sc. degree students.

We will review applications on a rolling basis and will hire the right person as soon as we find them, so please apply early.

Aalto University’s employees should apply for the position via our internal HR system Workday (Internal Jobs) by using their existing Workday user account (not via the external webpage for open positions). If you are a student or visitor at Aalto University, please apply with your personal email address (not aalto.fi) via

For more information about the role, please contact Doctoral Researcher Saska Tirronen (advisor) by email: saska.tirronen@aalto.fi. In recruitment process related questions, please contact HR Advisor Johanna Haapalainen (hr-elec@aalto.fi).

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