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Title: :  A Survey on Single and Multi-Speaker Voice Recognition In Android
PaperId: :  3920
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 3 Issue 1 2017
DUI:    16.0415/IJARIIE-3920
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Tanmay NaskarD Y Patil College of Engineering, Akurdi, Pune
Anket SahD Y Patil College of Engineering, Akurdi, Pune
Akshay MenseD Y Patil College of Engineering, Akurdi, Pune
Neha PatekarD Y Patil College of Engineering, Akurdi, Pune
Nikhatjahan KankurtiD Y Patil College of Engineering, Akurdi, Pune
Neeta DeshpandeD Y Patil College of Engineering, Akurdi, Pune
Suvarna KadamD Y Patil College of Engineering, Akurdi, Pune

Abstract

Computer Engineering
Voice biometric, Voice Recognition, Vice identification, Multi Speakers, MFCC, LPCC, VAD, Smartphone Application.
For communication voice is the most preferred natural form and the foremost goal of developing this system is to make interaction with machines as easy as possible. Voice recognition and speech recognition are considered same however, they have distinct meanings. Speech recognition detects only the words, while the voice recognition disregards the language or anything linguistic for that matter and detects the identity of the person speaking. In this given paper, we discuss two parts, In the first part of the paper, we discuss the means of processing the speech signal using the Mel Frequency Cepstral Coefficient and the basics of voice characteristics. At hardware level the problem of voice recognition had been already implemented, but here we propose a solution with higher level of accuracy on the software end. Moreover, both single speaker and multi-speaker single recognition application being developed on android is discussed in the second part. The multispeaker feature enables the system to identify more than one person speaking by splitting the input into smaller blocks and treating each block as a single speaker identification problem. For the case of single speaker recognition, the algorithm is able to correctly identify the individual 90.3% of the time. In the multispeaker recognition case, up to 3 individuals are correctly identified roughly 40% of the time. For a database consisting of 415 samples, the average time for identification is 220ms.

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IJARIIE Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, and Suvarna Kadam. "A Survey on Single and Multi-Speaker Voice Recognition In Android" International Journal Of Advance Research And Innovative Ideas In Education Volume 3 Issue 1 2017 Page 1547-1552
MLA Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, and Suvarna Kadam. "A Survey on Single and Multi-Speaker Voice Recognition In Android." International Journal Of Advance Research And Innovative Ideas In Education 3.1(2017) : 1547-1552.
APA Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, & Suvarna Kadam. (2017). A Survey on Single and Multi-Speaker Voice Recognition In Android. International Journal Of Advance Research And Innovative Ideas In Education, 3(1), 1547-1552.
Chicago Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, and Suvarna Kadam. "A Survey on Single and Multi-Speaker Voice Recognition In Android." International Journal Of Advance Research And Innovative Ideas In Education 3, no. 1 (2017) : 1547-1552.
Oxford Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, and Suvarna Kadam. 'A Survey on Single and Multi-Speaker Voice Recognition In Android', International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 1, 2017, p. 1547-1552. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Single_and_Multi_speaker_Voice_Recognition_in_Android_ijariie3920.pdf (Accessed : 03 December 2018).
Harvard Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, and Suvarna Kadam. (2017) 'A Survey on Single and Multi-Speaker Voice Recognition In Android', International Journal Of Advance Research And Innovative Ideas In Education, 3(1), pp. 1547-1552IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Single_and_Multi_speaker_Voice_Recognition_in_Android_ijariie3920.pdf (Accessed : 03 December 2018)
IEEE Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, and Suvarna Kadam, "A Survey on Single and Multi-Speaker Voice Recognition In Android," International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 1, pp. 1547-1552, Jan-Feb 2017. [Online]. Available: http://ijariie.com/AdminUploadPdf/Single_and_Multi_speaker_Voice_Recognition_in_Android_ijariie3920.pdf [Accessed : 03 December 2018].
Turabian Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, and Suvarna Kadam. "A Survey on Single and Multi-Speaker Voice Recognition In Android." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 3 number 1 (03 December 2018).
Vancouver Tanmay Naskar, Anket Sah, Akshay Mense, Neha Patekar, Nikhatjahan Kankurti, Neeta Deshpande, and Suvarna Kadam. A Survey on Single and Multi-Speaker Voice Recognition In Android. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2017 [Cited : 03 December 2018]; 3(1) : 1547-1552. Available from: http://ijariie.com/AdminUploadPdf/Single_and_Multi_speaker_Voice_Recognition_in_Android_ijariie3920.pdf
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