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. As speech interaction with computers becomes more
pervasive in activities such as the telephone, financial transactions
and information retrieval from speech databases, the utility of
automatically identifying a speaker is based solely on vocal
characteristic. This paper emphasizes on text dependent speaker
identification, which deals with detecting a particular speaker from a
known population. The system prompts the user to provide speech
utterance. System identifies the user by comparing the codebook of
speech utterance with those of the stored in the database and lists,
which contain the most likely speakers, could have given that speech
utterance. The speech signal is recorded for N speakers further the
features are extracted. Feature extraction is done by means of LPC
coefficients, calculating AMDF, and DFT. The neural network is
trained by applying these features as input parameters. The features
are stored in templates for further comparison. The features for the
speaker who has to be identified are extracted and compared with the
stored templates using Back Propogation Algorithm. Here, the
trained network corresponds to the output; the input is the extracted
features of the speaker to be identified. The network does the weight
adjustment and the best match is found to identify the speaker. The
number of epochs required to get the target decides the network
performance.
[1] S. kasuriya1, V. Achariyakulporn, C. Wutiwiwatchai, C. Tanprasert,
Text-dependent speaker identification via telephone based on dtw and
mlp. 22nd floor, Gypsum-Metropolitan Building, Sri-Ayudhaya
Rd.,Rachathewi, Bangkok 10400, Thailand
[2] Monte, J. Hernando,X.Mir├│,A. Adolf Dpt.TSC.Universitat Politécnica
de Catalunya Barcelona.Spain ,Text independent speaker identification
on noisy environments by means of self organizing maps Dpt. TSC.
Universitat Politécnica de Catalunya, Barcelona, Spain.
[3] A.N. Iyer, B. Y. Smolenski, R. E. Yantorno J. Cupples, S. Wenndt,
Speaker identification improvement using the usable speech concept.
Speech Processing Lab, Temple University 12th & Norris Streets,
Philadelphia, PA 19122,Air Force Research Laboratory/IFEC, 32
Brooks Rd. Rome NY 13441-4514.
[4] Lawrence Rabiner- "Fundamentals of Speech Recognition" Pearson
Education Speech Processing Series. Pearson Education Publication.
[5] Lawrence Rabiner- "Digital Processing of Speech Signals" Pearson
Education Speech Processing Series. Pearson Education Publication.
[6] Picton P.H.- "Introduction to Neural Networks", Mc Graw Hill
Publication.
[7] Ben Gold & Nelson Morgan -"Speech and Audio Signal Processing."
John Wiley and Sons.
[8] Duane Hanselman & Bruce Littlefield-"mastering Matlab-A
comprehensive Tutorial & reference" Prentice Hall International
Editions
[9] Proakis Manolakis "Digital Signal Processing Principles, Algorithms
and applications " Prentice -Hall India
[10] John G Proakis & Vinay K Ingle-"Digital Signal Processing Using
malab "Thomson Brooks/cole
[11] Jacek .M.Zurada "Introduction to artificial Neural Systems"
[12] http/www.electronicsletters.com
[13] http/www.DspGuru.com
[14] http/www.mathworks.com
[15] http/www.bores.com
[16] www.ieee.org/discover
[1] S. kasuriya1, V. Achariyakulporn, C. Wutiwiwatchai, C. Tanprasert,
Text-dependent speaker identification via telephone based on dtw and
mlp. 22nd floor, Gypsum-Metropolitan Building, Sri-Ayudhaya
Rd.,Rachathewi, Bangkok 10400, Thailand
[2] Monte, J. Hernando,X.Mir├│,A. Adolf Dpt.TSC.Universitat Politécnica
de Catalunya Barcelona.Spain ,Text independent speaker identification
on noisy environments by means of self organizing maps Dpt. TSC.
Universitat Politécnica de Catalunya, Barcelona, Spain.
[3] A.N. Iyer, B. Y. Smolenski, R. E. Yantorno J. Cupples, S. Wenndt,
Speaker identification improvement using the usable speech concept.
Speech Processing Lab, Temple University 12th & Norris Streets,
Philadelphia, PA 19122,Air Force Research Laboratory/IFEC, 32
Brooks Rd. Rome NY 13441-4514.
[4] Lawrence Rabiner- "Fundamentals of Speech Recognition" Pearson
Education Speech Processing Series. Pearson Education Publication.
[5] Lawrence Rabiner- "Digital Processing of Speech Signals" Pearson
Education Speech Processing Series. Pearson Education Publication.
[6] Picton P.H.- "Introduction to Neural Networks", Mc Graw Hill
Publication.
[7] Ben Gold & Nelson Morgan -"Speech and Audio Signal Processing."
John Wiley and Sons.
[8] Duane Hanselman & Bruce Littlefield-"mastering Matlab-A
comprehensive Tutorial & reference" Prentice Hall International
Editions
[9] Proakis Manolakis "Digital Signal Processing Principles, Algorithms
and applications " Prentice -Hall India
[10] John G Proakis & Vinay K Ingle-"Digital Signal Processing Using
malab "Thomson Brooks/cole
[11] Jacek .M.Zurada "Introduction to artificial Neural Systems"
[12] http/www.electronicsletters.com
[13] http/www.DspGuru.com
[14] http/www.mathworks.com
[15] http/www.bores.com
[16] www.ieee.org/discover
@article{"International Journal of Electrical, Electronic and Communication Sciences:54081", author = "R.V Pawar and P.P.Kajave and S.N.Mali", title = "Speaker Identification using Neural Networks", abstract = "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. As speech interaction with computers becomes more
pervasive in activities such as the telephone, financial transactions
and information retrieval from speech databases, the utility of
automatically identifying a speaker is based solely on vocal
characteristic. This paper emphasizes on text dependent speaker
identification, which deals with detecting a particular speaker from a
known population. The system prompts the user to provide speech
utterance. System identifies the user by comparing the codebook of
speech utterance with those of the stored in the database and lists,
which contain the most likely speakers, could have given that speech
utterance. The speech signal is recorded for N speakers further the
features are extracted. Feature extraction is done by means of LPC
coefficients, calculating AMDF, and DFT. The neural network is
trained by applying these features as input parameters. The features
are stored in templates for further comparison. The features for the
speaker who has to be identified are extracted and compared with the
stored templates using Back Propogation Algorithm. Here, the
trained network corresponds to the output; the input is the extracted
features of the speaker to be identified. The network does the weight
adjustment and the best match is found to identify the speaker. The
number of epochs required to get the target decides the network
performance.", keywords = "Average Mean Distance function,Backpropogation, Linear Predictive Coding, MultilayeredPerceptron,", volume = "1", number = "12", pages = "1797-5", }