I have extracted MFCC, Chroma features from the dataset. Can anyone help me to tell what should be next step?
Contact me in case you wish to for more discussion. We need to perform time-frequency analysis of the voice signal.
Have a look at : https://medium.com/syncedreview/clone-a-voice-in-five-seconds-with-this-ai-toolbox-f3f116b11281
https://cloud.google.com/speech-to-text/docs/transcription-model
https://www.cmswire.com/digital-asset-management/9-voice-datasets-you-should-know-about/
The basic approach is to create multi-dimensional feature vectors using the extracted MFCC features, and then use clustering algorithms like k-means to group into clusters. To visually observe the clusters PCA can be used to reduce dimensions to 2 so that a graph plot can be generated.
This can be easily done in the Eidos system. *The Eidos-X++ system differs from other artificial intelligence systems in the following parameters:
*- it was developed in a universal setting, independent of the subject area. Therefore, it is universal and can be applied in many subject areas (http://lc.kubagro.ru/aidos/index.htm);
*- it is in full open free access (http://lc.kubagro.ru/aidos/_Aidos-X.htm) and has all the relevant source texts (http://lc.kubagro.ru/__AIDOS-X.txt);
*- it is one of the first domestic systems of artificial intelligence of the personal level, i.e. it does not take special training in the field of technologies of artificial intelligence from the user (there is an act of introduction of system "Eidos" in 1987) (http://lc.kubagro.ru/aidos/aidos02/PR-4.htm);
*- it provides stable identification in a comparable form of strength and direction of cause-effect relationships in incomplete noisy interdependent (nonlinear) data of very large dimension of numerical and non-numerical nature, measured in different types of scales (nominal, ordinal and numerical) and in different units of measurement (i.e. does not impose strict requirements to the data that cannot be performed, and processes the data that can);
*- it contains a large number of local (supplied with the installation) and cloud educational and scientific applications (currently 31 and 252 (http://aidos.byethost5.com/Source_data_applications/WebAppls.htm), respectively) (http://lc.kubagro.ru/aidos/Presentation_Aidos-online.pdf);
*- it supports on-line environment of knowledge accumulation and is widely used all over the world (http://aidos.byethost5.com/map5.php);
*- it provides multilingual interface support in 51 languages. The language databases are included in the installation and can be replenished automatically;
*- the most time-consuming, computationally, are the operations of the synthesis models and implements recognition using graphic processing unit (GPU) where some tasks can only support up to several thousand times; the solution of these tasks is intelligent processing of big data, big information and big knowledge;
*- it provides transformation of the initial empirical data into information, and its knowledge and solution using this knowledge of classification problems, decision support and research of the subject area by studying its system-cognitive model, generating a very large number of tabular and graphical output forms (development of cognitive graphics), many of which have no analogues in other systems (examples of forms can be found in: http://lc.kubagro.ru/aidos/aidos18_LLS/aidos18_LLS.pdf);
*- it well imitates the human style of thinking: gives the results of the analysis, understandable to experts according to their experience, intuition and professional competence.
*- instead of making almost impossible demands on the source data (such as the normality of distribution, absolute accuracy and complete repetitions of all combinations of factor values and their complete independence and additivity), the automated system-cognitive analysis (ASC-analysis) offers to process this data without any preliminary processing and thereby transform it into information, and then transform this information into knowledge by applying it to achieve goals (i.e. for the management) and solving problems of classification, decision support, and meaningful empirical research of the domain being modeled.
*What is the strength of the approach implemented in Eidos system? The strength is implementing an approach whose effectiveness does not depend on what we think about the subject area or whether we think at all. It generates models directly based on empirical data, rather than based on our understanding of the mechanisms for implementing patterns in this data. This is why Eidos models are effective, even if our understanding of the subject area is incorrect or totally absent.
*And this as well is the weakness of this approach implemented in Eidos system. Models of the Eidos system are phenomenological models, i.e. they do not reflect the mechanisms of determination, but only the fact and nature of determination.
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