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Publisher: Springer-Verlag (Total: 2352 journals)

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Showing 1 - 200 of 2352 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 1)
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: 23, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 3)
Abdominal Imaging     Hybrid Journal   (Followers: 15, 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: 26, 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: 28, 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: 18, 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: 12, 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: 6, SJR: 0.822, CiteScore: 2)
Acta Neurologica Belgica     Hybrid Journal   (Followers: 1, 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: 2, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 15, 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: 24, 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: 17, SJR: 1.005, CiteScore: 2)
Adsorption     Hybrid Journal   (Followers: 4, 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: 19, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 56, 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: 29, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 4, 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: 6)
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: 10, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 20, 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     Hybrid Journal   (Followers: 14, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Hybrid Journal   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 12, 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: 8, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 16, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 4, 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: 21, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 14, 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: 5, 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: 19, 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: 32, 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: 13, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 11, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 4, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 12)
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: 14, 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: 8, 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: 2, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 45, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 18, SJR: 0.21, CiteScore: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 3, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 49, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 5, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 8, 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   (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: 67, 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: 24, 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: 21, SJR: 0.225, CiteScore: 0)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6, SJR: 0.542, CiteScore: 1)
Aquaculture Intl.     Hybrid Journal   (Followers: 25, SJR: 0.591, CiteScore: 2)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 2)
Aquatic Ecology     Hybrid Journal   (Followers: 36, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 13, 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: 63, 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: 5, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 148, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5, 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: 16, 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: 8, 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: 15, 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: 1, 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: 9, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 5, SJR: 0.504, CiteScore: 1)
Asia Pacific Education Review     Hybrid Journal   (Followers: 12, 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: 2, 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: 9)
Asian J. of Criminology     Hybrid Journal   (Followers: 6, SJR: 0.543, CiteScore: 1)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 3, 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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Journal Cover
International Journal of Speech Technology
Journal Prestige (SJR): 0.22
Citation Impact (citeScore): 1
Number of Followers: 8  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1572-8110 - ISSN (Online) 1381-2416
Published by Springer-Verlag Homepage  [2352 journals]
  • Robust noise MKMFCC–SVM automatic speaker identification
    • Authors: Osama S. Faragallah
      Pages: 185 - 192
      Abstract: This paper proposes robust noise automatic speaker identification (ASI) scheme named MKMFCC–SVM. It based on the Multiple Kernel Weighted Mel Frequency Cepstral Coefficient (MKMFCC) and support vector machine (SVM). Firstly, the MKMFCC is employed for extracting features from degraded audio and it uses multiple kernels such as the exponential and tangential and for MFCC’s weighting. Secondly, the extracted features are then categorized with the SVM classification technique. A comparative study is performed between the proposed MKMFCC–SVM and the MFCC–SVM ASI schemes using the MKMFCC and MFCCs with five schemes for extracting features from telephone-analogous and noisy-like degraded audio signals. Experimental tests prove that the proposed MKMFCC–SVM ASI scheme yields higher identification rate in noise presence or degradation.
      PubDate: 2018-06-01
      DOI: 10.1007/s10772-018-9494-9
      Issue No: Vol. 21, No. 2 (2018)
       
  • Chhattisgarhi speech corpus for research and development in automatic
           speech recognition
    • Authors: Narendra D. Londhe; Ghanahshyam B. Kshirsagar
      Pages: 193 - 210
      Abstract: Automatic speech recognition (ASR) is a computerized interface which allows humans to communicate with machine in a way of its natural conversation. ASR has wide range of applications in various fields such as language development in young children, telecommunications, as an assistive device for hearing impaired etc. Performance of ASR system is greatly influenced by the database used for its implementation. In this paper, we are discussing about building a speech corpus for a rare but important Indian dialect Chhattisgarhi. This speech corpus consists of 100 unique isolated words and four speech scripts aggregating 67 sentences, recorded from total 478 native speakers. These words were selected from English to Chhattisgarhi dictionary published by Chhattisgarh Rajbhasha Aayog and scripts from Chhattisgarhi literature and newspaper articles. This dataset has been collected travelling over 60% geographical area of the Chhattisgarh state. Finally, a valuable speech corpus for the first time have been prepared for Chhattisgarhi with an aim to enhance the speech research. The successful extermination of speech recognition for both isolated and continuous speech samples have been demonstrated on the prepared database.
      PubDate: 2018-06-01
      DOI: 10.1007/s10772-018-9496-7
      Issue No: Vol. 21, No. 2 (2018)
       
  • Continuous Punjabi speech recognition model based on Kaldi ASR toolkit
    • Authors: Jyoti Guglani; A. N. Mishra
      Pages: 211 - 216
      Abstract: In this paper, continuous Punjabi speech recognition model is presented using Kaldi toolkit. For speech recognition, the extraction of Mel frequency cepstral coefficients (MFCC) features and perceptual linear prediction (PLP) features were extracted from Punjabi continuous speech samples. The performance of automatic speech recognition (ASR) system for both monophone and triphone model i.e., tri1, tri2 and tri3 model using N-gram language model is reported. The performance of ASR system were computed in terms of word error rate (WER). A significant reduction in WER was observed using the tri phone model over mono phone model ASR .Also the performance of ASR using tri3 model is improved over tri2 model and the performance of tri2 model is improved over tri1 model ASR. Further, it was found that MFCC feature provides higher speech recognition accuracy than PLP features for continuous Punjabi speech.
      PubDate: 2018-06-01
      DOI: 10.1007/s10772-018-9497-6
      Issue No: Vol. 21, No. 2 (2018)
       
  • Combined distributed incremental affine projection algorithm for acoustic
           echo cancellation
    • Authors: Long Shi; Haiquan Zhao
      Pages: 383 - 390
      Abstract: To improve the performance of the conventional distributed incremental affine projection algorithm (DIAPA) in acoustic echo cancellation, a combined distributed incremental affine projection algorithm is proposed in this brief. The proposed algorithm combines two DIAPA equipped with different projection orders by employing a mixing scalar parameter, where one with high projection order guarantees fast convergence rate for the initial stage and the other with low projection order maintains small misalignment for the steady stage. The mixing scalar parameter is endowed with capability to adapt itself to be close to one in the initial stage and approximate to zero in the steady-state stage. Moreover, the analysis of the mixing scalar parameter is presented. We also take into account the computational complexity of the proposed algorithm. The simulations conducted in the system identification and acoustic echo cancellation experiments illustrate the superiority of our findings.
      PubDate: 2018-06-01
      DOI: 10.1007/s10772-018-9512-y
      Issue No: Vol. 21, No. 2 (2018)
       
  • Distant speech processing for smart home: comparison of ASR approaches in
           scattered microphone network for voice command
    • Authors: Benjamin Lecouteux; Michel Vacher; François Portet
      Abstract: Voice command in multi-room smart homes for assisting people in loss of autonomy in their daily activities faces several challenges, one of them being the distant condition which impacts ASR performance. This paper presents an overview of multiple techniques for fusion of multi-source audio (pre, middle, post fusion) for automatic speech recognition for in-home voice command. The robustness of the models of speech is obtained by adaptation to the environment and to the task. Experiments are based on several publicly available realistic datasets with participants enacting activities of daily life. The corpora were recorded in natural condition, meaning background noise is sporadic, so there is no extensive background noise in the data. The smart home is equipped with one or two microphones in each room, the distance between them being larger than 1 m. An evaluation of the most suited techniques improves voice command recognition at the decoding level, by using multiple sources and model adaptation. Although Word Error Rate (WER) is between 26 and 40%, Domotic Error Rate (identical to the WER, but at the level of the voice command) is less than 5.8% for deep neural network models, the method using Feature space Maximum Likelihood Linear Regression (fMLLR) with speaker adaptation training and Subspace Gaussian Mixture Model (SGMM) exhibits comparable results.
      PubDate: 2018-06-02
      DOI: 10.1007/s10772-018-9520-y
       
  • Text normalization with convolutional neural networks
    • Abstract: Text normalization is a critical step in the variety of tasks involving speech and language technologies. It is one of the vital components of natural language processing, text-to-speech synthesis and automatic speech recognition. Convolutional neural networks (CNNs) have proven their superior performance to recurrent architectures in various application scenarios, like neural machine translation, however their ability in text normalization was not exploited yet. In this paper we investigate and propose a novel CNNs based text normalization method. Training, inference times, accuracy, precision, recall, and F1-score were evaluated on an open-source dataset. The performance of CNNs is evaluated and compared with a variety of different long short-term memory (LSTM) and Bi-LSTM architectures with the same dataset.
      PubDate: 2018-05-30
      DOI: 10.1007/s10772-018-9521-x
       
  • Speech analysis and synthesis with a refined adaptive sinusoidal
           representation
    • Authors: Youcef Tabet; Mohamed Boughazi; Saddek Afifi
      Abstract: This paper explores common speech signal representations along with a brief description of their corresponding analysis–synthesis stages. The main focus is on adaptive sinusoidal representations where a refined model of speech is suggested. This model is referred to as Refined adaptive Sinusoidal Representation (R_aSR). Based on the performance of the recently suggested adaptive Sinusoidal Models of speech, significant refinements are proposed at both the analysis and adaptive stages. First, a quasi-harmonic representation of speech is used in the analysis stage in order to obtain an initial estimation of the instantaneous model parameters. Next, in the adaptive stage, an adaptive scheme combined with an iterative frequency correction mechanism is used to allow a robust estimation of model parameters (amplitudes, frequencies, and phases). Finally, the speech signal is reconstructed as a sum of its estimated time-varying instantaneous components after an interpolation scheme. Objective evaluation tests prove that the suggested R_aSR achieves high quality reconstruction when applied in modeling voiced speech signals compared to state-of-the-art models. Moreover, transparent perceived quality was attained using the R_aSR according to results obtained from listening evaluation tests.
      PubDate: 2018-05-15
      DOI: 10.1007/s10772-018-9519-4
       
  • Automatic syllabification of speech signal using short time energy and
           vowel onset points
    • Authors: Leena Mary; Anil P. Antony; Ben P. Babu; S. R. Mahadeva Prasanna
      Abstract: This paper describes a language independent method for automatic syllabification of speech signal. This method utilizes the valleys in short time energy (STE) contour and location of vowel onset points (VOP) for marking the syllable boundaries. In the proposed method, automatic syllabification is performed in three steps. First, long silence/pause regions are marked with the help of speech/non-speech detection. Then VOPs are located from the Hilbert Envelope of LP residual. The existence of more than one VOP in a continuous speech region (identified using speech/non-speech detection in the first step) is an indication of syllable boundaries within the region. Location with minimum energy in the STE contour between two consecutive VOP is identified as the syllable boundary. Since automatic VOP detection algorithm fails to detect some of the VOPs, certain syllable boundaries will be missed. Therefore, at the third step, additional syllable boundaries are detected from STE contour by fixing a valley threshold which is equal to the mean value of STE corresponding to each speech region between two consecutive syllable boundaries. This method is evaluated for 50 sentences each in read, extempore and conversational mode speech of Malayalam and Bengali languages. Overall accuracy of 80% is obtained with ± 50 ms tolerance with reference to manually marked syllable boundaries for this database. Method also shows good accuracy in case of TIMIT and NTIMIT data without tuning of thresholds and other parameters. This method is useful for applications that do not require exact syllable boundaries, rather a meaningful separation of syllables. Application of this technique for prosody based emotion recognition is illustrated using Emo-DB German emotional database.
      PubDate: 2018-05-09
      DOI: 10.1007/s10772-018-9517-6
       
  • Performance analysis of adaptive variational mode decomposition approach
           for speech enhancement
    • Authors: Rashmirekha Ram; Mihir Narayan Mohanty
      Abstract: Speech enhancement is an important pre-processing task in the area of speech processing research. Many techniques have been applied in this area since four/five decades. With progressive research it occupies a special position in various fields like engineering, medicine, society and security. Adaptive algorithms found effective for such cases and are utilized in this problem. The work is based on decomposition method using variational mode decomposition (VMD) technique, where the decomposed components signify the frequency characteristics of the signal. Since Wiener filtering is used in VMD inherently, it is modified with the least mean squares (LMS) adaptive algorithm for good accuracy and adaptability in this work. Different noises like Babble noise, Street noise, and Exhibition noise are considered and the corresponding signals are decomposed into five intrinsic mode functions (IMFs). Basically, the lower modes are of high frequency and noisy; whereas the higher mode IMFs contain the low and medium frequency components and are considered as the enhanced signal. The results of the proposed algorithm are found excellent as compared to earlier techniques. The resultant wave forms are visually observed and the sound is verified for audible range. Also different measuring parameters are considered for its performance measure. It is measured in terms of signal-to-noise ratio (SNR), segmental signal to noise ratio (SegSNR), perceptual evaluation of speech quality (PESQ) and log spectral distance (LSD). The technique is verified with standard database NOIZEUS for 0, 5, 10, 15 dB respectively and also in real world case.
      PubDate: 2018-04-24
      DOI: 10.1007/s10772-018-9515-8
       
  • An efficient wavelet-based adaptive filtering algorithm for automatic
           blind speech enhancement
    • Authors: Mohamed Djendi
      Abstract: In this paper, we address the problem of speech enhancement by adaptive filtering algorithms. A particular attention has been paid to the backward blind source separation (BBSS) algorithm and its use in crosstalk resistant speech enhancement applications. In this paper, we propose to implement the BBSS algorithm in the wavelet-domain. The proposed backward wavelet BBSS (WBBSS) algorithm is then used in speech enhancement application when important crosstalk interferences are presents. The new WBBSS algorithm shows better performances in terms of convergence speed and steady state in comparison with the classical BBSS one. The performances properties of the proposed algorithm are evaluated in term of segmental SNR (SegSNR), segmental mean square error (SegMSE), and cepstral distance (CD) criteria. The obtained results have confirmed the best performance of the proposed WBBSS algorithm in a lot of situations when blind noisy observations are available.
      PubDate: 2018-04-21
      DOI: 10.1007/s10772-018-9514-9
       
  • Singing voice separation using mono-channel mask
    • Authors: Pallavi P. Ingale; Sanjay L. Nalbalwar
      Abstract: Separating singing voice from monaural song recording is a highly difficult task. Still it is important because it has many applications such as singer identification, lyrics recognition, and melody extraction. Difficulty arises due to many musical instruments involved and time-varying spectral overlap between singing voice and music. The goal of singing voice separation is to extract singing voice from the given monaural song recording with minimum artefacts and musical interference. We propose a three stage system for singing voice separation which helps to improve intelligibility and perceptual quality of the separated output. In the first stage, modified sub-harmonic summation algorithm finds pitch of the singing voice and its harmonic components. Here, we create a binary mask. In the second stage, frames i.e. the masked spectral amplitudes are classified as singing and non-singing frames by using a combination of Gammatone frequency cepstral coefficients (GFCC) and Mel-frequency cepstral coefficients (MFCC) features. Lastly, mono-channel mask is created and signal amplitude correction is done using kurtosis measure. We synthesize the estimate of singing voice using both binary mask and mono-channel mask. It is observed that the singing voice separated using mono-channel mask improves the GNSDR score. Performance of the proposed system is compared with the other methods, where it presents excellent improvement in terms of GNSDR. It produces higher GNSDR scores in case of two different datasets.
      PubDate: 2018-04-18
      DOI: 10.1007/s10772-018-9509-6
       
  • Combined classification method for prosodic stress recognition in Farsi
           language
    • Authors: D. Gharavian; M. Sheikhan; Sh. S. Ghasemi
      Abstract: Employing stress in speech can transfer more information to a listener but makes more problems in speech recognition. The first step toward stressed speech recognition is the recognition of boundaries in stressed speech. In this research, the boundaries of prosodic stress were extracted in Farsi stressed sentences. The acoustic and prosodic features were used to train hidden Markov models for stress boundaries recognition. Using fast correlation-based filter (FCBF) method, the efficient features were selected for stress recognition. The influence of different feature sets on stress boundaries recognition performance was evaluated in this study. Based on this evaluation, a combined classifier scheme was proposed. Experimental results showed that the proposed combined model improved the stress boundaries detection performance by 12% as compared to the baseline model. So, the final recognition rate of the proposed classifier was 85% for prosodic stress boundaries recognition.
      PubDate: 2018-04-17
      DOI: 10.1007/s10772-018-9508-7
       
  • Adaptive framing based similarity measurement between time warped speech
           signals using Kalman filter
    • Authors: Wasiq Khan; Keeley Crockett; Muhammad Bilal
      Abstract: Similarity measurement between speech signals aims at calculating the degree of similarity using acoustic features that has been receiving much interest due to the processing of large volume of multimedia information. However, dynamic properties of speech signals such as varying silence segments and time warping factor make it more challenging to measure the similarity between speech signals. This manuscript entails further extension of our research towards the adaptive framing based similarity measurement between speech signals using a Kalman filter. Silence removal is enhanced by integrating multiple features for voiced and unvoiced speech segments detection. The adaptive frame size measurement is improved by using the acceleration/deceleration phenomenon of object linear motion. A dominate feature set is used to represent the speech signals along with the pre-calculated model parameters that are set by the offline tuning of a Kalman filter. Performance is evaluated using additional datasets to evaluate the impact of the proposed model and silence removal approach on the time warped speech similarity measurement. Detailed statistical results are achieved indicating the overall accuracy improvement from 91 to 98% that proves the superiority of the extended approach on our previous research work towards the time warped continuous speech similarity measurement.
      PubDate: 2018-04-17
      DOI: 10.1007/s10772-018-9511-z
       
  • A multi-tier security system (SAIL) for protecting audio signals from
           malicious exploits
    • Authors: N. Sasikaladevi; K. Geetha; K. N. Venkata Srinivas
      Abstract: This paper proposes a multi-tier SegmEntation ECC Desegmentation (SEED) model to suit audio cryptosystem for Securing Audio sIgnal (SAIL) based on discrete wavelet transform and elliptic curve encryption. It is aimed with the prospect of enhancing the level of security in digital audio communication for unreliable public networks. The proposed SAIL system works as a multitier SEED model by performing segmentation, DWT compression, ECC encryption and desegmentation. In the reverse process, this multitier model proceeds with segmentation, decryption, decompression, and desegmentation. The novelty of this work relies on the adoption of ECC for encryption as it is first of its kind in audio streaming. The selection of appropriate ECC curve is a real challenge, and complex multiplication method has been applied. ECC has been chosen for encryption as it has been identified as a discrete logarithm problem which is resistant to be attacked by quantum computers. The performance of the recommended SAIL cryptosystem has been tested using different audio samples characterizing human voice, animal voice and Instrumental music. Analysis of the proposed model shows the effectiveness for fast audio encryption as it works on compressed data and also computationally simple. Various statistical analysis have been done on the proposed model, and the obtained result ratifies better level protection of audio signals from different security threats and can be recommended for multi channel audio processing.
      PubDate: 2018-04-13
      DOI: 10.1007/s10772-018-9510-0
       
  • Robust front-end for audio, visual and audio–visual speech
           classification
    • Authors: Lucas D. Terissi; Gonzalo D. Sad; Juan C. Gómez
      Abstract: This paper proposes a robust front-end for speech classification which can be employed with acoustic, visual or audio–visual information, indistinctly. Wavelet multiresolution analysis is employed to represent temporal input data associated with speech information. These wavelet-based features are then used as inputs to a Random Forest classifier to perform the speech classification. The performance of the proposed speech classification scheme is evaluated in different scenarios, namely, considering only acoustic information, only visual information (lip-reading), and fused audio–visual information. These evaluations are carried out over three different audio–visual databases, two of them public ones and the remaining one compiled by the authors of this paper. Experimental results show that a good performance is achieved with the proposed system over the three databases and for the different kinds of input information being considered. In addition, the proposed method performs better than other reported methods in the literature over the same two public databases. All the experiments were implemented using the same configuration parameters. These results also indicate that the proposed method performs satisfactorily, neither requiring the tuning of the wavelet decomposition parameters nor of the Random Forests classifier parameters, for each particular database and input modalities.
      PubDate: 2018-04-13
      DOI: 10.1007/s10772-018-9504-y
       
  • Application of non-negative frequency-weighted energy operator for vowel
           region detection
    • Authors: Ramakrishna Thirumuru; Anil Kumar Vuppala
      Abstract: In this paper, a novel technique has been proposed for the vowel region detection from the continuous speech using an envelope of the derivative of the speech signal, which is a non-negative, frequency-weighted energy operator. The proposed vowel region detection method is implemented using a two-stage algorithm. The first stage of vowel region detection consists of speech signal analysis to detect vowel onset points (VOP) and vowel end-points (VEP) using an instantaneous energy contour obtained from the envelope of the derivative of a speech signal. The VOPs and VEPs are spotted using the peak-finding algorithm based upon the first order Gaussian differentiator. The next stage consists of removal of spurious vowel regions and the correction of hypothesized VOP and VEP locations using combined cues obtained from the uniformity of epoch intervals and strength of the excitation of the speech signal. Performance of the proposed method for detecting vowel regions from the speech signal is evaluated using TIMIT acoustic-phonetic speech corpus. The proposed approach resulted in significantly high detection rate and less false alarm rate compared to the state-of-the-art methods in both clean and noisy environments.
      PubDate: 2018-04-10
      DOI: 10.1007/s10772-018-9505-x
       
  • Emirati-accented speaker identification in each of neutral and shouted
           talking environments
    • Authors: Ismail Shahin; Ali Bou Nassif; Mohammed Bahutair
      Abstract: This work is devoted to capturing Emirati-accented speech database (Arabic United Arab Emirates database) in each of neutral and shouted talking environments in order to study and enhance text-independent Emirati-accented “speaker identification performance in shouted environment” based on each of “first-order circular suprasegmental hidden Markov models (CSPHMM1s), second-order circular suprasegmental hidden Markov models (CSPHMM2s), and third-order circular suprasegmental hidden Markov models (CSPHMM3s)” as classifiers. In this research, our database was collected from 50 Emirati native speakers (25 per gender) uttering eight common Emirati sentences in each of neutral and shouted talking environments. The extracted features of our collected database are called “Mel-Frequency Cepstral Coefficients (MFCCs)”. Our results show that average Emirati-accented speaker identification performance in neutral environment is 94.0, 95.2, and 95.9% based on CSPHMM1s, CSPHMM2s, and CSPHMM3s, respectively. On the other hand, the average performance in shouted environment is 51.3, 55.5, and 59.3% based, respectively, on “CSPHMM1s, CSPHMM2s, and CSPHMM3s”. The achieved “average speaker identification performance in shouted environment based on CSPHMM3s” is very similar to that obtained in “subjective assessment by human listeners”.
      PubDate: 2018-03-28
      DOI: 10.1007/s10772-018-9502-0
       
  • Speech recognition with reference to Assamese language using novel fusion
           technique
    • Authors: Sruti Sruba Bharali; Sanjib Kr. Kalita
      Abstract: This paper describes the implementation of a speech recognition system in Assamese language. The database for this research work consists of a vocabulary of ten Assamese words. The models for speech recognition have been trained using Hidden Markov Model, Vector Quantization technique and I-vector technique. Two new fusion methods have been proposed in this research study by combining the three techniques.
      PubDate: 2018-03-23
      DOI: 10.1007/s10772-018-9501-1
       
  • Manner of articulation based Bengali phoneme classification
    • Authors: Tanmay Bhowmik; Shyamal Kumar Das Mandal
      Abstract: A phoneme classification model has been developed for Bengali continuous speech in this experiment. The analysis was conducted using a deep neural network based classification model. In the first phase, phoneme classification task has been performed using the deep-structured classification model along with two baseline models. The deep-structured model provided better overall classification accuracy than the baseline systems which were designed using hidden Markov model and multilayer Perceptron respectively. The confusion matrix of all the Bengali phonemes generated by the classification model is observed, and the phonemes are divided into nine groups. These nine groups provided better overall classification accuracy of 98.7%. In the next phase of this study, the place and manner of articulation based phonological features are detected and classified. The phonemes are regrouped into 15 groups using the manner of articulation based knowledge, and the deep-structured model is retrained. The system provided 98.9% of overall classification accuracy this time. This is almost equal to the overall classification accuracy which was observed for nine phoneme groups. But as the nine phoneme groups are redivided into 15 groups, the phoneme confusion in a single group became less which leads to a better phoneme classification model.
      PubDate: 2018-03-09
      DOI: 10.1007/s10772-018-9498-5
       
  • Low rank sparse decomposition model based speech enhancement using
           gammatone filterbank and Kullback–Leibler divergence
    • Authors: Nasir Saleem; Gohar Ijaz
      Abstract: In speech enhancement systems, the key stage is to estimate noise which generally requires prior speech or noise models. However, it is difficult to obtain such prior models sometimes. This paper presents a speech enhancement algorithm which does not require prior knowledge of speech and noise, and is based on low-rank and sparse matrix decomposition model using gammatone filterbank and Kullback–Leibler divergence to estimate noise and speech by decomposing the input noisy speech magnitude spectra into low-rank noise and sparse speech parts, respectively. According to the proposed technique, noise signals are assumed as low-rank components because noise spectra within different time frames are usually highly correlated with each other; while the speech signals are considered as sparse components because they are relatively sparse in time–frequency domain. Based on these assumptions, we have developed an alternative speech enhancement algorithm to separate the speech and noise magnitude spectra by imposing rank and sparsity constraints, with which the enhanced time-domain speech can be constructed from sparse matrix The proposed technique is significantly different from existing speech enhancement techniques as it enhances noisy speech in an uncomplicated manner, without need of noise estimation algorithm to find noise-only excerpts for noise estimation. Moreover, it can obtain improved performance in low SNR conditions, and does not need to know the exact distribution of noise signals. Experimental results have showed that proposed technique can perform better than conventional techniques in many types of strong noise conditions, in terms of yielding less residual noise, lower speech distortion and better overall speech quality. An important improvement in terms of the PESQ, SNRSeg, SIG and BAK is observed with the proposed algorithm over baseline algorithms.
      PubDate: 2018-03-08
      DOI: 10.1007/s10772-018-9500-2
       
 
 
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