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Publisher: Springer-Verlag (Total: 2351 journals)

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Showing 1 - 200 of 2351 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 2)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 25, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 8, SJR: 0.752, CiteScore: 3)
Abdominal Radiology     Hybrid Journal   (Followers: 17, SJR: 0.866, CiteScore: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4, SJR: 0.439, CiteScore: 0)
Academic Psychiatry     Full-text available via subscription   (Followers: 28, SJR: 0.53, CiteScore: 1)
Academic Questions     Hybrid Journal   (Followers: 8, SJR: 0.106, CiteScore: 0)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 31, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (SJR: 0.232, CiteScore: 1)
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.367, CiteScore: 0)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.675, CiteScore: 1)
Acta Biotheoretica     Hybrid Journal   (Followers: 4, SJR: 0.284, CiteScore: 1)
Acta Diabetologica     Hybrid Journal   (Followers: 19, SJR: 1.587, CiteScore: 3)
Acta Endoscopica     Hybrid Journal   (Followers: 1)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.769, CiteScore: 1)
Acta Geochimica     Hybrid Journal   (Followers: 7, SJR: 0.24, CiteScore: 1)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 3, SJR: 0.305, CiteScore: 1)
Acta Geophysica     Hybrid Journal   (Followers: 11, SJR: 0.312, CiteScore: 1)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.588, CiteScore: 3)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.517, CiteScore: 1)
Acta Mathematica     Hybrid Journal   (Followers: 13, SJR: 7.066, CiteScore: 3)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.452, CiteScore: 1)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6, SJR: 0.379, CiteScore: 1)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.27, CiteScore: 0)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.208, CiteScore: 0)
Acta Mechanica     Hybrid Journal   (Followers: 21, SJR: 1.04, CiteScore: 2)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.607, CiteScore: 2)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 7, SJR: 0.576, CiteScore: 2)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.638, CiteScore: 1)
Acta Neurochirurgica     Hybrid Journal   (Followers: 7, SJR: 0.822, CiteScore: 2)
Acta Neurologica Belgica     Hybrid Journal   (Followers: 2, SJR: 0.376, CiteScore: 1)
Acta Neuropathologica     Hybrid Journal   (Followers: 4, SJR: 7.589, CiteScore: 12)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.334, CiteScore: 1)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 3, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 18, SJR: 0.605, CiteScore: 1)
Activitas Nervosa Superior     Hybrid Journal   (SJR: 0.147, CiteScore: 0)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 8, SJR: 0.103, CiteScore: 0)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 25, SJR: 0.72, CiteScore: 2)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 9)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 18, SJR: 1.005, CiteScore: 2)
Adsorption     Hybrid Journal   (Followers: 5, SJR: 0.703, CiteScore: 2)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4, SJR: 0.698, CiteScore: 1)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 37, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 20, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 58, SJR: 1.09, CiteScore: 1)
Advances in Gerontology     Partially Free   (Followers: 8, SJR: 0.144, CiteScore: 0)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 32, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 3, SJR: 0.475, CiteScore: 2)
Advances in Polymer Science     Hybrid Journal   (Followers: 45, SJR: 1.04, CiteScore: 3)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 1.075, CiteScore: 3)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 7)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.517, CiteScore: 1)
Aerobiologia     Hybrid Journal   (Followers: 3, SJR: 0.673, CiteScore: 2)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 11, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 21, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.235, CiteScore: 0)
AGE     Hybrid Journal   (Followers: 7)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.39, CiteScore: 1)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.67, CiteScore: 2)
Agricultural Research     Hybrid Journal   (Followers: 6, SJR: 0.276, CiteScore: 1)
Agriculture and Human Values     Open Access   (Followers: 14, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Open Access   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Open Access   (Followers: 15, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 9, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 14, SJR: 1.792, CiteScore: 3)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4, SJR: 0.862, CiteScore: 3)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 0)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.583, CiteScore: 1)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 1.095, CiteScore: 1)
Algorithmica     Hybrid Journal   (Followers: 9, SJR: 0.56, CiteScore: 1)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.234, CiteScore: 0)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 5, SJR: 1.11, CiteScore: 3)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 3)
AMBIO     Hybrid Journal   (Followers: 10, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 16, SJR: 0.951, CiteScore: 3)
American J. of Community Psychology     Hybrid Journal   (Followers: 29, SJR: 1.329, CiteScore: 2)
American J. of Criminal Justice     Hybrid Journal   (Followers: 9, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 18, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 5, SJR: 0.181, CiteScore: 0)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.611, CiteScore: 1)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 22, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 16, SJR: 0.35, CiteScore: 0)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.135, CiteScore: 3)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.211, CiteScore: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 6, SJR: 0.536, CiteScore: 1)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 6)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 32, SJR: 0.978, CiteScore: 3)
Anatomical Science Intl.     Hybrid Journal   (Followers: 3, SJR: 0.367, CiteScore: 1)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.177, CiteScore: 5)
Animal Cognition     Hybrid Journal   (Followers: 20, SJR: 1.389, CiteScore: 3)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.192, CiteScore: 0)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.097, CiteScore: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4, SJR: 0.438, CiteScore: 0)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.429, CiteScore: 0)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.197, CiteScore: 1)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 17, SJR: 1.042, CiteScore: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 4, SJR: 0.932, CiteScore: 1)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 34, SJR: 0.579, CiteScore: 1)
Annals of Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 2)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.228, CiteScore: 1)
Annals of Hematology     Hybrid Journal   (Followers: 15, SJR: 1.043, CiteScore: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 11, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 5, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 13)
Annals of Regional Science     Hybrid Journal   (Followers: 8, SJR: 0.614, CiteScore: 1)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 9, SJR: 0.239, CiteScore: 1)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 15, SJR: 1.986, CiteScore: 4)
Annals of Telecommunications     Hybrid Journal   (Followers: 9, SJR: 0.223, CiteScore: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 1.495, CiteScore: 1)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.834, CiteScore: 2)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.22, CiteScore: 3)
APOPTOSIS     Hybrid Journal   (Followers: 9, SJR: 1.424, CiteScore: 4)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2, SJR: 0.294, CiteScore: 1)
Applications of Mathematics     Hybrid Journal   (Followers: 3, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 44, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 18, SJR: 0.21, CiteScore: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 4, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 50, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 6, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 9, SJR: 0.488, CiteScore: 1)
Applied Intelligence     Hybrid Journal   (Followers: 13, SJR: 0.6, CiteScore: 2)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.319, CiteScore: 1)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 9, SJR: 0.886, CiteScore: 1)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (Followers: 1, SJR: 0.17, CiteScore: 0)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.461, CiteScore: 1)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 66, SJR: 1.182, CiteScore: 4)
Applied Physics A     Hybrid Journal   (Followers: 10, SJR: 0.481, CiteScore: 2)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 25, SJR: 0.74, CiteScore: 2)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.519, CiteScore: 2)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 12, SJR: 0.316, CiteScore: 1)
Applied Solar Energy     Hybrid Journal   (Followers: 22, SJR: 0.225, CiteScore: 0)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 7, SJR: 0.542, CiteScore: 1)
Aquaculture Intl.     Hybrid Journal   (Followers: 26, SJR: 0.591, CiteScore: 2)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 2)
Aquatic Ecology     Hybrid Journal   (Followers: 37, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 14, SJR: 1.109, CiteScore: 3)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.303, CiteScore: 1)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 2, SJR: 0.319, CiteScore: 1)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 21, SJR: 1.052, CiteScore: 2)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.224, CiteScore: 0)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.725, CiteScore: 1)
Archival Science     Hybrid Journal   (Followers: 68, SJR: 0.745, CiteScore: 2)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.186, CiteScore: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3, SJR: 0.909, CiteScore: 1)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 3.93, CiteScore: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 155, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6, SJR: 1.41, CiteScore: 5)
Archives of Dermatological Research     Hybrid Journal   (Followers: 7, SJR: 1.006, CiteScore: 2)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 14, SJR: 0.773, CiteScore: 2)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 17, SJR: 0.956, CiteScore: 2)
Archives of Microbiology     Hybrid Journal   (Followers: 9, SJR: 0.644, CiteScore: 2)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 9, SJR: 1.146, CiteScore: 2)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 0.71, CiteScore: 2)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 10, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 17, SJR: 1.541, CiteScore: 5)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 0.973, CiteScore: 2)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 17, SJR: 1.274, CiteScore: 3)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 0.946, CiteScore: 3)
ArgoSpine News & J.     Hybrid Journal  
Argumentation     Hybrid Journal   (Followers: 6, SJR: 0.349, CiteScore: 1)
Arid Ecosystems     Hybrid Journal   (Followers: 2, SJR: 0.2, CiteScore: 0)
Arkiv för Matematik     Hybrid Journal   (Followers: 2, SJR: 0.766, CiteScore: 1)
Arnold Mathematical J.     Hybrid Journal   (Followers: 1, SJR: 0.355, CiteScore: 0)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2, SJR: 0.839, CiteScore: 2)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 11, SJR: 0.937, CiteScore: 2)
Artificial Intelligence Review     Hybrid Journal   (Followers: 18, SJR: 0.833, CiteScore: 4)
Artificial Life and Robotics     Hybrid Journal   (Followers: 10, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 5, SJR: 0.504, CiteScore: 1)
Asia Pacific Education Review     Hybrid Journal   (Followers: 13, SJR: 0.479, CiteScore: 1)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 16, SJR: 1.185, CiteScore: 2)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 13, SJR: 0.353, CiteScore: 1)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 3, SJR: 0.187, CiteScore: 0)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 19, SJR: 0.855, CiteScore: 1)
Asian Business & Management     Hybrid Journal   (Followers: 9, SJR: 0.378, CiteScore: 1)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 11)
Asian J. of Criminology     Hybrid Journal   (Followers: 6, SJR: 0.543, CiteScore: 1)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 4, SJR: 0.548, CiteScore: 1)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.183, CiteScore: 0)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  
Astronomy and Astrophysics Review     Hybrid Journal   (Followers: 22, SJR: 3.385, CiteScore: 5)

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Similar Journals
Journal Cover
International Journal of Speech Technology
Journal Prestige (SJR): 0.22
Citation Impact (citeScore): 1
Number of Followers: 9  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1572-8110 - ISSN (Online) 1381-2416
Published by Springer-Verlag Homepage  [2351 journals]
  • The automatic assessment of the severity of dysphonia
    • Abstract: Perceptual evaluation of the patient’s voice is the most commonly used method in everyday clinical practice. We propose an automatic approach for the prediction of severity of some types of organic and functional dysphonia. By means of an unsupervised learning method, we have demonstrated that acoustic parameters measured on different phonetic classes are suitable for modelling the four grade assessments of the specialists (RBH subjective scale from 0 to 3). In this study, the overall hoarseness H was examined. Four specialists were asked to determine the severity of dysphonia. A k-means cluster analysis was performed for the decision of each specialist separately; the average accuracy of the four-grade classification was 0.46. The four-grade classification has been surprisingly close to the subjective judgements. Moreover, automatic estimation of severity of dysphonia was also determined. Linear regression and RBF kernel regression models were compared. The average rating of the four specialists were used as target in the experiments. Low RMSE and high correlation measures were obtained between the automatically predicted severity and perceptual assessments. The best RMS value of H was 0.45 for the model with RBF kernel, however, a simpler linear model provided the highest correlation value of 0.85, using only eight acoustic parameters.
      PubDate: 2019-03-11
       
  • Multistage classification scheme to enhance speech emotion recognition
    • Abstract: During the past decades, emotion recognition from speech has become one of the most explored areas in affective computing. These systems lack universality due to multilingualism. Research in this direction is restrained due to unavailability of emotional speech databases in various spoken languages. Arabic is one such language, which faces this inadequacy. The proposed work aims at developing a speech emotion recognition system for Arabic speaking community. A speech database with elicited emotions—anger, happiness, sadness, disgust, surprise and neutrality are recorded from 14 subjects, who are non-native, but proficient speakers in the language. The prosodic, spectral and cepstral features are extracted after pre-processing. Subsequently the features were subjected to single stage classification using supervised learning methods viz. Support vector machine and Extreme learning machine. The performance of the speech emotion recognition systems implemented are compared in terms of accuracy, specificity, precision and recall. Further analysis is carried out by adopting three multistage classification schemes. The first scheme followed a two stage classification by initially identifying gender and then the emotions. The second used a divide and conquer approach, utilizing cascaded binary classifiers and the third, a parallel approach by classification with individual features, followed by a decision logic. The result of the study depicts that, these multistage classification schemes an bring improvement in the performance of speech emotion recognition system compared to the one with single stage classification. Comparable results were obtained for same experiments carried out using Emo-DB database.
      PubDate: 2019-03-08
       
  • Preface: Special Section: Advances in Speech, Music and Audio Signal
           processing (Articles 1–13)
    • PubDate: 2019-03-04
       
  • Combining evidences from Hilbert envelope and residual phase for detecting
           replay attacks
    • Abstract: In this work, the Hilbert envelope of the linear prediction (LP) residual and the residual phase have been explored for detecting replay attacks. The two source features namely, LP residual Hilbert envelope mel frequency cepstral coefficient (LPRHEMFCC) and residual phase cepstral coefficient (RPCC) are used for replay detection. From the signal perspectives, Hilbert envelope represents the amplitude information of LP residual samples. Residual phase represents to excitation information present in the sequence of LP residual samples. Hence, both can be considered as two components of the raw LP residual signal. In this direction, score level fusion of LPRHEMFCC and RPCC features is compared with a third source feature named as, residual mel frequency cepstral coefficient (RMFCC) derived from the raw LP residual using LP analysis. Comparative analysis has been performed using Gaussian mixtures model-universal background model (GMM-UBM) ASV experiments (IITG-MV replay database) and spoof detection experiments (ASVspoof 2017 database). For IITG-MV database, relative (RFAR-ZFAR) improvements of 86.10% (males), 27.45% (females) and 54.14% (whole-set) are achieved for (LPRHEMFCC + RPCC) + MFCC combination over RMFCC + MFCC combination. The RFAR and ZFAR stands for false acceptance rate under replay attacks and zero effort impostor attacks, respectively. In terms of tandem-detection cost function (t-DCF) metrics, the obtained relative improvements are 40.50%, 13.13% and 26.16%, respectively. For ASVspoof 2017 database, relative EER improvements of 11.72% and 6.74% are achieved for (LPRHEMFCC + RPCC) + MFCC and (LPRHEMFCC + RPCC) + CQCC over RMFCC + MFCC and RMFCC + CQCC, respectively. These observations justify the usefulness of exploring Hilbert envelope and residual phase components of the LP residual over direct processing of the LP residual signal for detecting replay attacks. Moreover, score level fusion of LPRHEMFCC, RPCC and CQCC provides 8.86% EER.
      PubDate: 2019-03-04
       
  • Speech bandwidth extension using transform-domain data hiding
    • Abstract: A new transform-domain speech bandwidth extension algorithm is proposed to transmit the information about the missing speech frequencies over a hidden channel, i.e., the related spectral envelope and gain parameters are hidden within the narrowband speech signal using fast Fourier transform-based data hiding technique. The hidden information is recovered reliably at the receiving end to produce a wideband signal of much higher quality. Obtained results confirm that the excellent reconstructed wideband (RWB) signal quality of the proposed algorithm over the traditional methods.
      PubDate: 2019-03-04
       
  • Optimal prosodic feature extraction and classification in parametric
           excitation source information for Indian language identification using
           neural network based Q-learning algorithm
    • Abstract: Automatic language identification (LID) system has extensively recognized in a real world multilanguage speech specific applications. The formation speech is relying on the vocal tract area which explores the excitation source information for LID task. In this paper, LID system utilizes sub segmental, segmental and supra segmental features from Linear Prediction residual of speech signal, represents various native language speech excitation source information. The glottal flow derivative of speech signal is obtained through iterative adaptive inverse filtering method. Moreover, the prosodic features of speech signal are extracted using short time Fourier transform due to its capability to process non-stationary signals. Finally, the deep neural network based Q-learning (DNNQL) algorithm has been employed for identification of the class label for a specific language. Experimental validation of the proposed approach is carried out using Indian language recorded database. Finally, the proposed LID system approach is performing well with 97.3% accuracy compared to other machine learning based approaches.
      PubDate: 2019-03-01
       
  • Hidden-Markov-model based statistical parametric speech synthesis for
           Marathi with optimal number of hidden states
    • Abstract: Hidden Markov Model and Deep Neural Networks based Statistical Parametric Speech Synthesis systems, gain a significant attention from researchers because of their flexibility in generating speech waveforms in diverse voice qualities as well as in styles. This paper describes HMM-based speech synthesis system (SPSS) for the Marathi language. In proposed synthesis method, speech parameter trajectories used for synthesis are generated from the trained hidden Markov models (HMM). We have recorded our database of 5300 phonetically balanced Marathi sentences to train the context-dependent HMM with five, seven and nine hidden states. The subjective quality measures (MOS and PWP) shows that the HMMs with seven hidden states are capable of giving an adequate quality of synthesized speech as compared to five state and with less time complexity than seven state HMMs. The contextual features used for experimentation are inclusive of a position of an observed phoneme in a respective syllable, word, and sentence.
      PubDate: 2019-03-01
       
  • Multilingual query-by-example spoken term detection in Indian languages
    • Abstract: Spoken language processing poses to be a challenging task in multilingual and mixlingual scenario in linguistically diverse regions like Indian subcontinent. Common articulatory based framework is explored for the representation of phonemes of different languages. This framework is designed to handle typical features like aspirated plosives, nasalized vowels, combined letters, unvoiced retroflex plosive which are found in majority of Indian languages. It is trained with two languages. Different strategies like transfer training and joint training are studied to adapt English trained neural networks with smaller amount of Bangla data. It is observed that such training not only improves Query-by-Example Spoken Term Detection (QbE-STD) in the language of same language family like Hindi but also other Indian languages like Tamil and Telugu. While cross lingual adaptation of neural networks with a language specific softmax layer has been studied earlier in context of speech recognition, this work presents an architecture which is language independent uptil softmax layer. It is observed that this architecture has higher accuracy for unseen languages, is more compact and can be adapted more easily for new languages in comparison to the classic phoneme posteriorgrams based architecture.
      PubDate: 2019-03-01
       
  • Long short-term memory recurrent neural network architectures for Urdu
           acoustic modeling
    • Abstract: Recurrent neural networks (RNNs) have achieved remarkable improvements in acoustic modeling recently. However, the potential of RNNs have not been utilized for modeling Urdu acoustics. The connectionist temporal classification and attention based RNNs are suffered due to the unavailability of lexicon and computational cost of training, respectively. Therefore, we explored contemporary long short-term memory and gated recurrent neural networks Urdu acoustic modeling. The efficacies of plain, deep, bidirectional and deep-directional network architectures are evaluated empirically. Results indicate that deep-directional has an advantage over the other architectures. A word error rate of 20% was achieved on a hundred words dataset of twenty speakers. It shows 15% improvement over the baseline single-layer LSTMs. It has been observed that two-layer architectures can improve performance over single-layer, however the performance is degraded with further layers. LSTM architectures were compared with gated recurrent unit (GRU) based architectures and it was found that LSTM has an advantage over GRU.
      PubDate: 2019-03-01
       
  • Indonesian graphemic syllabification using a nearest neighbour classifier
           and recovery procedure
    • Abstract: An automatic syllabification, decomposing a word into syllables, is an important part in an automatic speech recognition (ASR) that uses both syllable-based acoustic and language models. It can be performed to either phoneme or grapheme sequences. The phonemic syllabification is more complex than the other since it requires a grapheme-to-phoneme conversion (G2P) as a previous process. It generally gives a high accuracy for many formal words but its accuracy may decrease for person-names. In contrast, the graphemic syllabification is simpler and more potential to be applied for person-names. This research focuses on developing a model of graphemic syllabification using a combination of phonotactic rules and Fuzzy k-nearest neighbour in every Class (FkNNC). The phonotactic rules are designed to find some deterministic syllabification points while FkNNC, as a statistical classifier, is expected to search the remaining stochastic syllabification points. A recovery procedure is proposed to correct the wrong syllabification points produced by FkNNC. Fivefold cross-validating on a dataset of 50k formal words, selected from the great dictionary of the Indonesian language, shows that the proposed model gives syllable error rate (SER) of 2.48% and the proposed recovery procedure reduces the SER to be 2.27%, which is higher than that produced by the phonemic syllabification (only 0.99%). But, this model is capable of handling a dataset of 15k high variance person-names with SER of 7.45% and the proposed recovery procedure reduces the SER to be 6.78%.
      PubDate: 2019-03-01
       
  • Continuous Tamil Speech Recognition technique under non stationary noisy
           environments
    • Abstract: In the last few years, the need for Continuous Speech Recognition system in Tamil language has been increased widely. In this research work, efficient Continuous Tamil Speech Recognition (CTSR) technique is proposed under non stationary noisy environments. This research work consists of two stages such as speech enhancement and modelling phase. In this, the modified Modulation Magnitude Estimation based Spectral Subtraction with Chi-Square Distribution based Noise Estimation (SS–NE) algorithm is proposed to enhance the noisy Tamil speech signal under various non-stationary noise environments. In order to extract the speech segments from the continuous speech, further the enhanced speech signal is segmented through the combination of short-time signal energy and spectral centroid features of the signal. In this work, 26 mel frequency cepstral coefficients per frame are found as optimal values and they are considered as acoustic feature vectors for each frame. In this research work, the Fuzzy C-Means (FCM) clustering is used in order to cluster the extracted feature vectors into discrete symbols. From the evaluation results, it is found that the optimal number of clusters ‘C’ as 5. Finally, Tamil speech from various speakers is recognized using Expectation Maximization Gaussian Mixture Model (EM-GMM) with 16 component densities under continuous measurements of labelled features from FCM clustering techniques in order to reduce the word error rate. From the simulated results, it is observed that the proposed FCM with EM-GMM model for CTSR improves the recognition accuracy from 1.2 to 4.4% when compared to the existing algorithms under different noisy environments by reducing the WER from 1.6 to 5.47%.
      PubDate: 2019-03-01
       
  • Voice signal processing for detecting possible early signs of
           Parkinson’s disease in patients with rapid eye movement sleep behavior
           disorder
    • Abstract: In this study we introduced a method for early detecting of Parkinson’s disease (PD) in patients with rapid eye movement sleep behavior disorder (RBD). Patients suffering from RBD are at extremely high risk (> 80%) for developing PD as well as other related neurodegenerative disorders. The database used in this study contains 30 PD patients in the very early stages, 50 RBD patient and 50 healthy subjects (HS). First, we created a model with a maximal accuracy of 85% of discrimination between PD and HS by testing different combinations of acoustic features along with different kernels of SVM and leave one subject out validation scheme. Based on that model, we tested 50 RBD patients in order to see whether they will belong to PD or HS groups. As a result we found 66% of RBD patients were classified as PD. Based on these foundlings we confirmed the existence of a correlation between RBD patients and early PD patients using speech analysis and thus, early PD signs can be reliably captured. These results will lead to the development of an embedded system for detecting the possible early signs of PD and other neurodegenerative diseases.
      PubDate: 2019-03-01
       
  • Noise reduction in speech signals using adaptive independent component
           analysis (ICA) for hands free communication devices
    • Abstract: This paper aims to remove the noise presents in speech signals during communication in all hands-free devices like mobile phone, video conferencing, teleconferencing conferencing etc. The existing noise reduction algorithms like an adaptive filter, time-varying and multiband adaptive gain control etc., have serious drawbacks. To enhance the algorithm for a better outcome an independent component analysis (ICA) based noise reduction is used. ICA is a statistical computational technique that divides the multisource signal into individual subcomponents. It is an active approach to cancel all of the ambient noise or a selective part of it without knowing the knowledge of the background noise. The adaptive nature of ICA in the proposed method makes the algorithm more robust in a real-time scenario. In the proposed method, the noisy speech signal is maximized by using kurtosis and negentropy cost functions of ICA to separate out the original speech signal from the noise. The simulations show that the proposed adaptive ICA method provides higher SNR compared to existing ICA methods and other conventional methods. Thus Adaptive ICA performs efficient noise cancellation in all real-time communication devices.
      PubDate: 2019-03-01
       
  • Replay spoofing countermeasures using high spectro-temporal resolution
           features
    • Abstract: The easy implementation of replay attacks by a fraudster poses a severe threat to automatic speaker verification (ASV) technology than the other spoofing attacks like speech synthesis and voice conversion. Replay attacks refer to an attack by a fraudster to get illegitimate access to an ASV system by playing back the speech sample collected from genuine target speaker. The significant cues that can differentiate between genuine and replay recordings are channel characteristics. To capture these characteristics, one need to extract features from the spectrum, which should have high spectral and temporal resolutions. Zero time windowing (ZTW) analysis of speech is one such time-frequency analysis technique, which results in high spectral and temporal resolution spectrum at each sampling instant. In this study, new features are proposed by applying cepstral analysis to ZTW spectrum. Experiments are performed on two publicly available replay attack databases namely BTAS 2016 and ASVspoof 2017. The first set of experiments are conducted using Gaussian mixture models to evaluate the potential of proposed features. Performance of the proposed system in terms of half total error rate is 0.75% and in terms of equal error rate is 14.75% on BTAS 2016 and ASVspoof 2017 evaluation sets respectively. A score level fusion is performed by using proposed features with previously proposed single frequency filtering cepstral coefficients. This fused result outperformed the previously reported best results on these two datasets.
      PubDate: 2019-03-01
       
  • Evaluating noise suppression methods for recovering the Lombard speech
           from vocal output in an external noise field
    • Abstract: Speech production is affected by noise due to the Lombard effect. The traditional method of investigation is through headphone delivery of noise to allow speech to be recorded in quiet, but this could create an occlusion effect artefact during speech production. It is also not directly applicable when wearing hearing protectors, hearing aids, or other devices due to physical interference by the headphones. In these situations, the Lombard effect needs to be elicited by an external noise field and speech recorded in the presence of noise. This is a more challenging measurement situation, but one that preserves perception of own voice and the surrounding noise in interaction with the hearing device worn. Two methods, direct waveform subtraction and adaptive noise cancellation, were evaluated for suppressing the background noise in the recorded speech..The effects of sound recording configuration on performance was investigated for two microphone types (omnidirectional and directional) at two distances (50 and 25 cm) in different noises and in the presence of real talker’s movement. Results show that the amount of noise reduction with both suppression methods is greater for fluctuating than continuous noises. Overall, the best recording configuration for noise reduction was with the omnidirectional microphone at 25 cm. Pitch extraction, energy level, and objective speech intelligibility and quality measures show that both suppression methods provide adequate noise reduction for SNRs as low as − 10 dB, which is suitable to successfully recover Lombard speech produced in an external noise field with open ears and when wearing hearing protectors.
      PubDate: 2019-03-01
       
  • Development and analysis of Punjabi ASR system for mobile phones under
           different acoustic models
    • Abstract: Speech technology is widely gaining importance in our daily life. Speech based mobile phone applications are becoming popular in masses due to their usability and ease of access. Speech technology is helping people, with disabilities like blindness and physical abnormalities, to access and control mobile phone applications through voice, without using keypad or touchpad. Punjabi is one of the widely spoken language in various parts of the world. In this paper, an automatic speech recognition (ASR) system for mobile phone applications in Punjabi has been proposed and implemented for four different acoustic models- context independent, context dependent untied, context dependent tied, and context dependent deleted interpolation models. The proposed ASR is evaluated at 4, 16, 32 and 64 GMMs for performance analysis in terms of parameters like accuracy, word error rate and storage space required. It is observed that context dependent untied models outperform others by having better accuracy and lower word error rate, while context independent models require less storage space than others. The choice of fruitful acoustic model depends upon the available storage space as well as desired recognition accuracy. Mobile phones having limited resources may use context independent models, while context dependent untied models can be used to develop ASR system for high end mobile phones.
      PubDate: 2019-03-01
       
  • Speech synthesis for glottal activity region processing
    • Abstract: The objective of this paper is to demonstrate the significance of combining different features present in the glottal activity region for statistical parametric speech synthesis (SPSS). Different features present in the glottal activity regions are broadly categorized as F0, system, and source features, which represent the quality of speech. F0 feature is computed from zero frequency filter and system feature is computed from 2-D based Riesz transform. Source features include aperiodicity and phase component. Aperiodicity component representing the amount of aperiodic component present in a frame is computed from Riesz transform, whereas, phase component is computed by modeling integrated linear prediction residual. The combined features resulted in better quality compared to STRAIGHT based SPSS both in terms of objective and subjective evaluation. Further, the proposed method is extended to two Indian languages, namely, Assamese and Manipuri, which shows similar improvement in quality.
      PubDate: 2019-03-01
       
  • Enhancement of esophageal speech obtained by a voice conversion technique
           using time dilated Fourier cepstra
    • Abstract: This paper presents a novel speaking-aid system for enhancing esophageal speech (ES). The method adopted in this paper aims to improve the quality of esophageal speech using a combination of a voice conversion technique and a time dilation algorithm. In the proposed system, a Deep Neural Network (DNN) is used as a nonlinear mapping function for vocal tract vector transformation. Then the converted frames are used to determine realistic excitation and phase vectors from the target training space using a frame selection algorithm. Next, in order to preserve speaker identity of the esophageal speakers, we use the source vocal tract features and propose to apply on them a time dilation algorithm to reduce the unpleasant esophageal noises. Finally the converted speech is reconstructed using the dilated source vocal tract frames and the predicted excitation and phase. DNN and Gaussian mixture model (GMM) based voice conversion systems have been evaluated using objective and subjective measures. Such an experimental study has been realized also in order to evaluate the changes in speech quality and intelligibility of the transformed signals. Experimental results demonstrate that the proposed methods provide considerable improvement in intelligibility and naturalness of the converted esophageal speech.
      PubDate: 2019-03-01
       
  • Blind multichannel identification based on Kalman filter and eigenvalue
           decomposition
    • Abstract: A noise-robust approach for blind multichannel identification is proposed on the basis of Kalman filter and eigenvalue decomposition. It is proved that the state vector composed of the multichannel impulse responses is nothing but the eigenvector corresponding to the maximum eigenvalue of the filtered state-error correlation matrix. This eigenvector can be computed iteratively with the so-called ‘power method’ to reduce the complexity of the algorithm. Furthermore, it is found that the computation of the inverse of the filtered state-error correlation matrix is much easier than itself, the wanted state vector can be computed from this inverse matrix with the so-called ‘inverse power method’. Therefore, two algorithms are proposed on the basis of the eigenvalue decomposition of the filtered state-error correlation matrix and its inverse matrix, respectively. In addition, for reducing the computing complexity of the proposed algorithms, matrix factorization such as QR-, LU- and Cholesky-factorizations are exploited to accelerate the computation of the algorithms. Simulations show that the proposed algorithms perform well over a wide range of the signal-to-noise ratio of the multichannel signals.
      PubDate: 2019-03-01
       
  • Temperature controlled PSO on optimizing the DBN parameters for phoneme
           classification
    • Abstract: Speech recognition has become an essential component to communicate with the latest gadgets and machines in ease through speech. Phoneme classification model for phonemes in Tamil continuous speech is built here by exploring the power of deep belief network (DBN), a powerful neural network architecture that is capable of learning complex problems. But building an efficient DBN highly relies on several parameters like number of layers, number of neurons, connection weights and bias. The effect of increasing the number of layers in DBN for phoneme recognition has been studied in our previous experiments. In addition, a methodology which employed particle swarm optimization (PSO) or its variants second generation PSO (SGPSO) and new method PSO (NMPSO) for optimizing the connection weights and bias of the DBN for phoneme classification were studied in our earlier work. Pre-training DBN with PSO faced the problem of particle stagnation and took longer time to converge, whereas DBN with SGPSO, NMPSO converges faster but still suffers from particle stagnation which prevents it from reaching an optimal solution. Here we try to minimize stagnation of particles in the population in addition to faster convergence by proposing a new improved PSO, named Temperature controlled TPSO to optimize the initial connection weights and bias parameters that controls the DBN efficiency. TPSO seems to converge faster with better optimizing the DBN connection weights and bias parameters when compared to the existing ones with reduced stagnation of population. The TPSO–DBN is designed and applied on a phoneme classification problem for Tamil continuous speech and found to classify phonemes comparatively better with a classification accuracy of 89.2%.
      PubDate: 2019-03-01
       
 
 
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