The task of speaker identification is to determine the identity of a speaker by machine. To recognize the voice, the voices must be familiar in the case of human beings as well as machines. The objective of speaker identification is to determine the identity of a speaker by machine on the basis of his/her voice. No identity is claimed by the user. GitHub Link:https://github.com/TrilokiDA/Speaker-Identification-from-Voice

1. Speaker Identification from
Voice
Guided by: Dr.S.R.Balasundaram Presented by: Triloki Gupta
(Professor) M.Tech(DataAnalytics)
205217006
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2. Content
● Introduction
● Motivation
● Problem statement
● Objective
● Model Architecture
● Features of model
● Implementation details
● About dataset
● Result
● Conclusion and Future work
● References
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3. Introduction
• The fundamental purpose of speech is communication, i.e., the
transmission of messages.
• The speech signal conveys information about the identity of
the speaker.
• The area of speaker identification is concerned with extracting
the identity of the person speaking the utterance.
• Recent development has made it possible to use this in the
security system.
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4. Cont..
● Two common recognition tasks are:
○ speaker verification (determining whether a speaker’s
claimed identity is true or false) and
○ speaker identification (classifying the identity of an
unknown voice among a set of speakers).
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5. Motivation
● Speaker identification makes it possible to use the speaker's voice to
verify their identity.
● And control access to services such as voice dialing, banking by
telephone, database access services, voice mail, security control for
confidential information areas, and remote access to computers etc.
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6. Problem Statement
● Understanding how to recognize complex, high-dimensional
voice/speech/audio data is one of the greatest challenges of our time.
● Traditional(GMMs) approach suffers from an inherent assumption of
linearity in speech signal dynamics. Such approaches are prone to
overfitting and have problems with generalization.
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7. Objective
● The objective of speaker identification is to determine the
identity of a speaker by machine on the basis of his/her voice.
● No identity is claimed by the user.
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8. Model Architecture
MLP CNN
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9. Cont..
RNN LSTM
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10. Work Flow
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11. Features of model
● The special structure such as local connectivity, weight sharing, non-linear
function, and pooling in CNNs exhibits some degree of invariance to small shifts
of speech features along the frequency axis, which is important to deal with
speaker and environment variations.
● RNN is Hidden state, which remembers some information about a sequence.
RNN have a “memory” which remembers all information about what has been
calculated. RNN work just fine when we are dealing with short-term
dependencies.
● LSTM is an artificial recurrent neural network (RNN) architecture. LSTM is
dealing with long-term dependencies rather than short-term dependencies.
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12. Implementation Details
● Creating Dataset of 14 speaker
● Data preprocessing
○ Feature extraction:
■ mfcc(Mel-frequency cepstral coefficients)
■ melspectogram(mel-scaled spectrogram)
■ chroma_stft(Short-Time Fourier Transform)
■ chroma_cqt(Constant-Q transform)
■ chroma_cens(Chroma Energy Normalized)
● Building Neural Network:
○ CNN
○ RNN
○ LSTM
● Person identification based on his/her voice 12
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13. Cont..
● Sample of feature in image:
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14. About dataset
● This dataset contains 1,330 voice recordings from 14 classes and each class
contains about 90 to 100 voice. Each class label is set with a speaker name.
● Feature extraction is done by mfcc (Mel-frequency cepstral coefficients),
melspectogram(mel-scaled spectrogram), chroma_stft (Short-Time Fourier
Transform), chroma_cqt (Constant-Q transform), and chroma_cens
(Chroma Energy Normalized). The neural network is trained by applying
these features as input parameters.
● From each voice, extracting 200 features by mfcc, melspectogram,
chroma_stft, chroma_cqt, and chroma_cens which means 40 from each.
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15. Results
● MLP:
○ Test acc = 98.35%, Train acc = 86.67%, Train loss = 0.8480,Test loss = 0.0321
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16. Cont..
● CNN:
○ Test acc = 99.17%, Train acc = 99.38 Train loss = 0.0261, Test loss = 0.0248
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17. Cont..
● RNN:
○ Test acc = 98.35%, Train acc = 96.04, Train loss = 0.1229, Test loss = 0.0358
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18. Cont..
● LSTM:
○ Test acc = 99.67%, Train acc = 99.58, Train loss = 0.0312, Test loss = 0.0091
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19. Cont..
● GRU:
○ Test acc = 97.52%, Train acc = 99.58 , Train loss = 0.0105, Test loss = 0.1984
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20. Conclusion and Future Work
● This system was able to identify 14 different speakers in a satisfactory way.
These speakers were the users from whom we took the samples to train the
system. The speaker identification system was tested using different samples
from those used to train it.
● The achieved test accuracy from MLP, CNN, RNN, LSTM, and GRU was
98.35%, 99.17%, 98.35%, 99.67%, and 97.52% respectively.
● The future work is like tagging the speaker from mix voice.
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21. References
[1] M. Schmidt and H. Gish, “Speaker identification via support vector classifiers”, 1996 IEEE International Conference on Acoustics, Speech, and
Signal Processing Conference Proceedings.
[2] Amirsina Torfi, Jeremy Dawson and Nasser M. Nasrabadi, “Text-Independent Speaker Verification Using 3D Convolutional Neural Networks,”
arXiv:1705.09422v7, 2018.
[3] Mirco Ravanelli and Yoshua Bengio, “Speaker recognition from raw waveform with SincNET,” arXiv:1808.00158v2, 2018.
[4] Chao Li, Xiaokong Ma, Bing Jiang, Xiangang Li, Xuewei Zhang, Xiao Liu, Ying Cao, Ajay Kannan and Zhenyao Zhu, “Deep Speaker: an End-
to-End Neural Speaker Embedding System”, arXiv:1705.02304v1, May 2017
[5] Roberto Togneri and Daniel Pullella, “An Overview of Speaker Identification: Accuracy and Robustness Issues”, IEEE Circuits and Systems
Magazine, 09 June 2011
[6] R.V Pawar, P.P.Kajave, and S.N.Mali, “Speaker Identification using Neural Networks”, World Academy of Science, Engineering and
Technology, 12 2005
[7] Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn, and Dong Yu, “Convolutional Neural Networks for Speech
Recognition”, IEEE/ACM Transaction on audio, speech, and language processing, vol. 22, no. 10, october 2014
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