Speech and Audio Processing

No abstract yet.

• MUHAMMED FATİH DEMİRCİ

No abstract yet.

• AYŞEGÜL GÜNDÜZ
• BETÜL ERDOĞDU ŞAKAR
• CEMAL OKAN ŞAKAR
• GÖRKEM SERBES

An increasing number of independent studies have con-firmed the vulnerability of automatic speaker verification (ASV) technology to spoofing. However, in comparison to that involving other biometric modalities, spoofing and countermea-sure research for ASV is still in its infancy. A current barrier to progress is the…

The performance of biometric systems based on automatic speaker recognition technology is severely degraded due to spoofing attacks with synthetic speech generated using different voice conversion (VC) and speech synthesis (SS) techniques. Various countermeasures are proposed to detect this type of at-tack, and in thi…

This letter introduces a deep learning (DL) framework for the classification of multiple signals in direction finding (DF) scenario via sensor arrays. Previous works in DL context mostly consider a single or two target scenario, which is a strong limitation in practice. Hence, in this letter, we propose a DL framework…

Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing counterm…

• CEMAL HANİLÇİ

In affective computing applications, access to labeled spontaneous affective data is essential for testing the designed algorithms under naturalistic and challenging conditions. Most databases available today are acted or do not contain audio data. We present a spontaneous audio-visual affective face database of affec…

• ÇİĞDEM EROĞLU ERDEM

No abstract yet.

• CEMAL OKAN ŞAKAR

Deep learning can be used for audio signal classification in a variety of ways. It can be used to detect and classify various types of audio signals such as speech, music, and environmental sounds. Deep learning models are able to learn complex patterns of audio signals and can be trained on large datasets to achieve…

It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification us…

• YÜCEL YEMEZ

It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification us…

• ENGİN ERZİN
• YÜCEL YEMEZ

It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification us…

• AHMET MURAT TEKALP
• ENGİN ERZİN