@Article{SouzaSoNaSoMaMa:2018:InReNo,
               author = "Souza, Francisco Carlos M. and Souza, Alinne C. Corr{\^e}a and 
                         Nakamura, Gilberto M. and Soares, Marinalva Dias and Mandr{\'a}, 
                         Patr{\'{\i}}cia Pupin and Macedo, Alessandra A.",
          affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Universidade de 
                         S{\~a}o Paulo (USP)} and {Universidade de S{\~a}o Paulo (USP)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Universidade de 
                         S{\~a}o Paulo (USP)}",
                title = "Investigating the recognition of non-articulatory sounds by using 
                         statistical tests and support vector machine",
              journal = "Advances in Intelligent Systems and Computing",
                 year = "2018",
               volume = "738",
                pages = "639--649",
             keywords = "Delayed speech development · Speech recognition methods · Machine 
                         learning · Automatic speech recognition.",
             abstract = "People with articulation and phonological disorders need training 
                         to plan and to execute sounds of speech. Compared to other 
                         children, children with Down Syndrome have significantly delayed 
                         speech development because they present developmental 
                         disabilities, mainly apraxia of speech. In practice, speech 
                         therapists plan and perform trainings of articulatory and 
                         non-articulatory sounds such as blow production and popping lips 
                         in order to assist speech production. Mobile applications can be 
                         integrated into the clinical treatment to transcend the boundaries 
                         of clinics and schedules and therefore reach more people at any 
                         time. The use of artificial intelligence and machine learning 
                         techniques can improve this kind of application. The aim of this 
                         pilot study is to assess speech recognition methods prioritizing 
                         the training of sounds for speech production, particularly the 
                         non-articulatory sounds. These methods apply Mel-Frequency 
                         Cepstrum Coefficients and Laplace transform to extract features, 
                         as well as traditional statistical tests and Support Vector 
                         Machine (SVM) to recognize sounds. This study also reports 
                         experimental results regarding the effectiveness of the methods on 
                         a set of 197 sounds. Overall, SVM provides higher accuracy.",
                  doi = "10.1007/978-3-319-77028-4_82",
                  url = "http://dx.doi.org/10.1007/978-3-319-77028-4_82",
                 issn = "2194-5357",
             language = "en",
           targetfile = "souza_investigating.pdf",
        urlaccessdate = "28 abr. 2024"
}