Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Systems that automatically detect voice pathologies are usually trained with recordings belonging to population of all ages.#R##N#Hover such an approach might be inadequate because of the acoustic variations in the voice caused by the natural aging process. In top of that, elder voices present some perturbations in quality similar to those related to voice disorders, which make the detection of pathologies more troublesome.  With this in mind, the study of methodologies which automatically incorporate information about speakers’ age, aiming at a simplification in the detection of voice disorders is of interest.  In this respect, the present paper introduces an age detector trained with normal and pathological voice, constituting a first step towards the study of age-dependent pathology detectors. The proposed system employs sustained vols of the Saarbrucken database from which two age groups are examinated: adults and elders. Mel frequency cepstral coefficients for characterization, and Gaussian mixture models for classification are utilized. In addition, fusion of vols at score level is considered to improve detection performance.  Results suggest that age might be effectively recognized using normal and pathological voices when using sustained vols as acoustical material, opening up possibilities for the design of automatic age-dependent voice pathology detection systems.
['Jorge Andrés Gómez García', 'Laureano Moro-Velázquez', 'Juan Ignacio Godino-Llorente', 'Germán Castellanos-Domínguez']
I am writing a research paper on a idea Automatic age detection in normal and pathological voice
[{"from": "human", "value": "I am writing a research paper on a idea Automatic age detection in normal and pathological voice"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Systems that automatically detect voice pathologies are usually trained with recordings belonging to population of all ages.#R##N#Hover such an approach might be inadequate because of the acoustic variations in the voice caused by the natural aging process. In top of that, elder voices present some perturbations in quality similar to those related to voice disorders, which make the detection of pathologies more troublesome.  With this in mind, the study of methodologies which automatically incorporate information about speakers\u2019 age, aiming at a simplification in the detection of voice disorders is of interest.  In this respect, the present paper introduces an age detector trained with normal and pathological voice, constituting a first step towards the study of age-dependent pathology detectors. The proposed system employs sustained vols of the Saarbrucken database from which two age groups are examinated: adults and elders. Mel frequency cepstral coefficients for characterization, and Gaussian mixture models for classification are utilized. In addition, fusion of vols at score level is considered to improve detection performance.  Results suggest that age might be effectively recognized using normal and pathological voices when using sustained vols as acoustical material, opening up possibilities for the design of automatic age-dependent voice pathology detection systems."}]