Speaker Identification using Machine Learning
Babu Pandipati1, R. Praveen Sam2
1Babu Pandipati*, Research Scholar, Research and Development Center, Bharathiyar University, Coimbatore.
2Dr. R. Praveen sam, Professors, Department of CSE, G.Pulla Reddy Engineering College, Kurnool.
Manuscript received on October 12, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 1216-1218 | Volume-9 Issue-1, November 2019. | Retrieval Number: L31791081219/2019©BEIESP | DOI: 10.35940/ijitee.L3179.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Whatever the modern achievement of deep learning for several terminology processing tasks, single-microphone, speaker-independent speech separation remains difficult for just two main things. The rest point is that the arbitrary arrangement of the goal and masker speakers in the combination (permutation problem), and also the following is the unidentified amount of speakers in the mix (output issue). We suggest a publication profound learning framework for speech modification, which handles both issues. We work with a neural network to project the specific time-frequency representation with the mixed-signal to a high-dimensional categorizing region. The time-frequency embeddings of the speaker have then made to an audience around corresponding attractor stage that is employed to figure out the time-frequency assignment with this speaker identifying a speaker using a blend of speakers together with the aid of neural networks employing deep learning. The purpose function for your machine is standard sign renovation error that allows finishing functioning throughout both evaluation and training periods. We assessed our system with all the voices of users three and two speaker mixes and also document similar or greater performance when compared with another advanced level, deep learning approaches for speech separation.
Keywords: Source Separation, Multi-Talker, Deep Clustering, Attractor Network.
Scope of the Article: Machine Learning