Mohammad Hossein Sedaaghi
Papers
1
Total Citations
44
H-Index
1
About
Mohammad Hossein Sedaaghi is a prominent researcher in speech processing and pattern recognition, with a particular focus on the intersection of acoustic modeling and classification systems. His work has significantly advanced the understanding of how gender and age information can be extracted from speech signals, a critical component for improving the accuracy of speaker recognition, speech emotion classification, and related technologies. His most-cited paper, "A Comparative Study of Gender and Age Classification in Speech Signals" (2009), with 44 citations, demonstrates the practical importance of building separate acoustic models for male and female speakers—a finding that has influenced the design of more robust speech-based systems. Beyond speech, his research extends to face recognition and video summarization, showcasing a broad expertise in multimodal biometrics and signal analysis. Sedaaghi’s contributions have provided foundational insights that enable more personalized and accurate human-computer interaction, making his work essential reading for students and researchers in speech technology and machine learning.
Research Focus
Key Achievements
Top Papers
- 1A Comparative Study of Gender and Age Classification in Speech Signals44 citations · 2009