Clear Voice, which originated from a paper presented by Gazi University Computer Engineering student Muhammed Emin Korkut at the 34th IEEE Signal Processing and Communications Applications Conference, makes speech more understandable by reducing background noise in podcasts, meetings, interviews, and training recordings.

Clear Voice, an AI-powered audio processing platform developed in Turkey, offers a practical solution for users who want to clean audio recordings taken in noisy environments via their browser. The platform aims to reduce elements that degrade recording quality, such as background noise, background sound, wind, electrical static, and room echo, while preserving the intelligibility and naturalness of the human voice.

Clear Voice is based on an academic research. The platform’s developer, Muhammed Emin Korkut, presented a paper titled **“Comparative Analysis of Deep Learning Models for Speech Enhancement in Noisy Environments”** at the 34th IEEE Signal Processing and Communications Applications Conference (SIU 2026). This work, which can be translated into Turkish as “Comparative Analysis of Deep Learning Models for Speech Enhancement in Noisy Environments,” focused on the performance of different deep learning models in noisy speech recordings.

During the research process, the models’ ability to suppress noise, preserve speech naturalness, and produce results within a usable timeframe were considered. The fact that each model showed different strengths under different recording conditions determined Clear Voice’s approach. Instead of applying a single model to all audio files, Korkut developed a multi-model structure that users could choose from according to their needs.

From paper to web-based product

The research presented at SIU 2026 experimentally compared different speech enhancement models. Clear Voice, on the other hand, translates the knowledge and experience gained in this study into a web application that can be used by users who do not have technical knowledge in the field of voice processing.

Muhammed Emin Korkut explains the emergence of the project as follows:

“Clear Voice emerged directly from the transformation of an academic research into a product. In the paper I presented at SIU 2026, I compared the performance of different deep learning models in noisy speech recordings. During the research, I saw that each model excelled under different conditions. Instead of leaving these results only in a paper, I wanted to transform them into an accessible platform that people could “use in their daily lives. This approach is also the basis of Clear Voice offering multiple artificial intelligence models.”

The platform has five model options with different processing power and voice smoothing characteristics. Depending on the quality of the recording and the expected result, the user can use one of the options ranging from V1 to V5. Lighter models focus on fast and basic noise removal processes, while advanced models offer more powerful processing options.

This structure is based on a work of art understanding that acknowledges that every audio recording is unique. The constant air conditioning sound in an online meeting recording may require different processes than the traffic and wind noise in an outdoor interview. Clear Voice aims to address these differences by giving the user the option to choose a model.

What does Clear Voice do?

Clear Voice is a web-based platform that allows users to upload audio files online and process them with artificial intelligence models. The user uploads an audio file, selects the model they want to use, and starts the process. After analyzing the speech and background noises, the system creates a cleaned output.

Once the process is complete, the user can listen to the original and cleaned versions of the recording comparatively. The resulting audio file can then be downloaded and used in editing, broadcasting, or archival work. The platform supports MP3, WAV, M4A, and FLAC formats.

Clear Voice’s main use cases are:

– Reducing background noise in podcast segments recorded at home or in the office
– Cleaning up traffic, wind, and ambient noise heard in field interviews
– Making speech more understandable in online meeting and interview recordings
– Reducing echo in training videos and lecture recordings
– Preparing video content, voiceovers, and archive recordings for broadcast

The platform targets users who don’t want to deal with the complex filters and parameters of professional audio editing software. Content creators, journalists, students, educators, and small teams can process their recordings through a single web interface.

Five different AI models

Clear Voice’s technical infrastructure includes five other deep learning-based audio optimization services. The models operate as independent services, and user requests are routed to the relevant model via the central application.

Multi-model architecture is one of the most obvious manifestations of the comparative study presented at SIU 2026. The platform does not operate on the assumption that a single algorithm will yield the exact same result for every recording. The aim is for users to be able to try different process options and choose the appropriate result for their recordings.

Audio uploading, model selection, process sequence management, and delivery of the result to the user all take place within a single interface. The ability to listen to brand new and processed audio files side-by-side helps the user evaluate the result based on their own recording.

Audio data privacy

Since audio recordings may contain private conversations, business meetings, or unpublished content, data confidentiality is among the platform’s key considerations. According to Clear Voice’s published confidentiality policy, audio files uploaded by users are not used to train public or publicly available AI models and are not shared with third parties.

Users can set an automatic deletion period for their documents via account settings. Document transfer is carried out via encrypted communication. Texts regarding the platform’s privacy, cookies, and terms of use can be accessed on the Clear Voice website.

Who is Muhammed Emin Korkut?

Muhammed Emin Korkut is a student in the Computer Engineering Department at Gazi University and an artificial intelligence developer. He conducts research on image processing, deep learning, voice technologies, and language models. His professional profile states that he works at DeepZeka Information Software Technology Industry and Trade Inc.

Korkut’s work in the field of artificial intelligence began in high school. His project, “Skin Cancer Diagnosis with Deep Learning Models,” received an Encouragement Award in the Software category of the 2023 TÜBİTAK 2204-A High School Students Research Projects Competition. Korkut’s project is registered in the Artificial Intelligence thematic area in TÜBİTAK’s official results list.

Korkut also participates in academic studies that combine health technologies with speech and text processing fields. In 2026, he was among the co-authors of the study titled **“A Multimodal Speech and Text-Based AI Clinical Decision Support System for Post-Traumatic Stress Disorder Risk Assessment”** at the International Conference on Electrical and Electronics Engineering. The study addressed an AI-based clinical decision support system that examines the semantic content and acoustic properties of speech together.

The Clear Voice project exemplifies how Korkut’s academic work in signal processing and deep learning is directly applied to the product development process. The approaches compared in the research provided the basis for developing a multimodal speech cleaning infrastructure accessible to users via a browser.