| Pathological assessment of patients’ speech signals using nonlinear dynamical analysis G Vaziri, F Almasganj, R Behroozmand Computers in biology and medicine 40 (1), 54-63, 2010 | 107 | 2010 |
| Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients’ speech signal with unilateral vocal fold paralysis R Behroozmand, F Almasganj Computers in Biology and Medicine 37 (4), 474-485, 2007 | 101 | 2007 |
| Lip-reading via a DNN-HMM hybrid system using combination of the image-based and model-based features MH Rahmani, F Almasganj 2017 3rd International Conference on Pattern Recognition and Image Analysis …, 2017 | 46 | 2017 |
| Support vector wavelet adaptation for pathological voice assessment NE Saeedi, F Almasganj, F Torabinejad Computers in biology and medicine 41 (9), 822-828, 2011 | 44 | 2011 |
| Audio-visual feature fusion via deep neural networks for automatic speech recognition MH Rahmani, F Almasganj, SA Seyyedsalehi Digital Signal Processing 82, 54-63, 2018 | 36 | 2018 |
| Voice disorder signal classification using m-band wavelets and support vector machine P Saidi, F Almasganj Circuits, Systems, and Signal Processing 34, 2727-2738, 2015 | 30 | 2015 |
| Acoustic features of vocalization signal in poultry health monitoring A Mahdavian, S Minaei, PM Marchetto, F Almasganj, S Rahimi, C Yang Applied Acoustics 175, 107756, 2021 | 29 | 2021 |
| A gaussian mixture model based cost function for parameter estimation of chaotic biological systems Y Shekofteh, S Jafari, JC Sprott, SMRH Golpayegani, F Almasganj Communications in Nonlinear Science and Numerical Simulation 20 (2), 469-481, 2015 | 28 | 2015 |
| Wavelet adaptation for automatic voice disorders sorting NE Saeedi, F Almasganj Computers in biology and medicine 43 (6), 699-704, 2013 | 27 | 2013 |
| Comparison of neural networks and support vector machines applied to optimized features extracted from patients' speech signal for classification of vocal fold inflammation R Behroozmand, F Almasganj Proceedings of the Fifth IEEE International Symposium on Signal Processing …, 2005 | 27 | 2005 |
| Improvement of automatic speech recognition systems via nonlinear dynamical features evaluated from the recurrence plot of speech signals SG Firooz, F Almasganj, Y Shekofteh Computers & Electrical Engineering 58, 215-226, 2017 | 26 | 2017 |
| Feature extraction based on speech attractors in the reconstructed phase space for automatic speech recognition systems Y Shekofteh, F Almasganj ETRI Journal 35 (1), 100-108, 2013 | 26 | 2013 |
| Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls A Mahdavian, S Minaei, C Yang, F Almasganj, S Rahimi, PM Marchetto Computers and electronics in agriculture 168, 105100, 2020 | 22 | 2020 |
| Autoregressive modeling of speech trajectory transformed to the reconstructed phase space for ASR purposes Y Shekofteh, F Almasganj Digital Signal Processing 23 (6), 1923-1932, 2013 | 22 | 2013 |
| A two-stage speech activity detection system considering fractal aspects of prosody S Shafiee, F Almasganj, B Vazirnezhad, A Jafari Pattern Recognition Letters 31 (9), 936-948, 2010 | 22 | 2010 |
| On the fractal self-similarity of laryngeal pathologies detection: The estimation of hurst parameter G Vaziri, F Almasganj, MS Jenabi 2008 International Conference on Information Technology and Applications in …, 2008 | 22 | 2008 |
| Local discriminant wavelet packet basis for voice pathology classification PT Hosseini, F Almasganj, T Emami, R Behroozmand, S Gharibzade, ... 2008 2nd International Conference on Bioinformatics and Biomedical …, 2008 | 22 | 2008 |
| Using Laplacian eigenmaps latent variable model and manifold learning to improve speech recognition accuracy A Jafari, F Almasganj Speech Communication 52 (9), 725-735, 2010 | 17 | 2010 |
| Statistical modeling of speech Poincaré sections in combination of frequency analysis to improve speech recognition performance A Jafari, F Almasganj, MN Bidhendi Chaos: An Interdisciplinary Journal of Nonlinear Science 20 (3), 2010 | 17 | 2010 |
| Hybrid statistical pronunciation models designed to be trained by a medium-size corpus B Vazirnezhad, F Almasganj, SM Ahadi Computer Speech & Language 23 (1), 1-24, 2009 | 17 | 2009 |
