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Speaker recognition system by abhishek mahajan

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Speaker recognition system by abhishek mahajan

  1. 1. SHREEJEE INSTITUTE OF TECHNOLOGY AND MANAGEMENT Speaker Recognition • Guided By:- Mr. Prakash Singh Panwar • By:- Rajpal Singh Chouhan • EC BRANCH 1ST YEAR
  2. 2. What is Speaker Recognition? Speaker Recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech signals. Speaker Recognition = Speaker Identification, Speaker Verification
  3. 3. Speaker Identification • a Whose voice is this? ? ? ??
  4. 4. Speaker Verification • a • Synonyms: authentication, detection. • User claims an identity. • System task: Accept or reject identity claim. Is this Ahmad’s voice ? ?
  5. 5. Model of Speaker Recognizer • a Fig -1 : Simple model of Speaker Recognizer . U Permitted to Access Hello, Mr. John
  6. 6. The Structure of Speaker Recognizer• a • Figure 2 :Functional Scheme of an ASR System. Feature Extraction Feature Vector Training Mode Recognition Speaker Modeling Classification Decision Logic Speaker #ID Speaker_1
  7. 7. Speech Signal Analysis Feature Extraction • a • - The aim is to extract the voice features to distinguish different phonemes of a language. 5 1 5 6 4 5 4 6 5 1 5 6 1 5 6 1 6 5 1 5 6 4 5 6 4 5 4 2 5 1 5 6 1 5 6 5
  8. 8. MFCC extraction • a Pre-emphasis DFT Mel filter banks Log(||2) IDFT Speech signal x(n) WINDOW x’(n) xt (n) Xt(k) Yt(m) MFCC yt (m)(k) MFCC means Mel-frequency cepstral coefficients that representation of the short-term power spectrum of a sound for audio processing. The MFCCs are the amplitudes of the resulting spectrum.
  9. 9. a • a Speech waveform of a phoneme “ae” After pre-emphasis and Hamming windowing Power spectrum MFCC
  10. 10. Speech Signal to Feature Vector • a 5 1 5 6 4 5 4 6 5 1 5 6 1 5 6 1 6 5 1 5 6 4 5 6 4 5 4 2 5 1 5 6 1 5 6 5
  11. 11. Vector Quantization (VQ) • aAIM of VQ : representation of large amounts of data by (few) prototype vectors. example: identification and grouping in clusters of similar data. assignment of feature vector  to the closest prototype w (similarity or distance measure, e.g. Euclidean distance )
  12. 12. Database Creation Process • a Database Speaker #1 Speaker #2 Speaker #3 Hello, Speaker #1 Speaker #1Speaker #2 Hello, Speaker #2
  13. 13. Speaker Identification • a Database #1 #2 #3 Speaker # ? Speaker # 1
  14. 14. Speaker Verification • a Database #1 #2 #3 Speaker # 1Accept 14
  15. 15. Database Creation Condition • a Table 1: Database description. Parameter Characteristics Language Bangla No. of speaker 5 Speech type Sentence reading Recording condition A normal room condition Audio Length 60-90 seconds Audio type Stereo Sample Format 16-bit PCM Sampling Frequency 8 KHz Bit Rate 1411 kbps
  16. 16. Speaker Recognition Result • a Table 3: Test result for speaker recognition system. Speaker No. of input Correct Incorrect Accuracy Speaker_1 5 5 0 100% Speaker_2 9 8 1 88.88% Speaker_3 6 6 0 100% Speaker_3 12 11 1 91.67% Speaker_4 8 8 0 100% Speaker_5 10 10 0 100% Total Speaker 50 48 2 96%
  17. 17. Applications • a • Transaction authentication – Toll fraud prevention – Telephone credit card purchases – Telephone brokerage (e.g., stock trading) • Access control – Physical facilities – Computers and data networks • Information retrieval – Customer information for call centers – Audio indexing (speech skimming device) • Forensics – Voice sample matching
  • kinguyenbanggia

    May. 22, 2019

about speaker recognition system by abhishek mahajan

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