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GritTec's Speaker-ID: Automatic Text Independent Speaker Identification


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  Downloads of SDK
Datasheet (34 Kb)
Trial Demo with console mode (21 Mb)
Trial Demo with GUI interface - GritTec Speaker-ID: The mobile client
Abstract of GritTec's Delivery
API Description (100 Kb)
Full SDK for Intel x86/x64 (74 Mb)
High Level SDK for Intel x86/x64 (60 Mb)
 

Overview

GritTec's Speaker-ID: Automatic Text Independent Speaker Identification (Version 3.00) is intended for automatic voice identification or voice verification of a speech signal of unknown speaker by paired comparing with speech signal of target speaker.

Designed algorithm of speaker identifications is based on duel comparison spectra features of unknown voice with the spectra features of target voice. Spectra features are calculated with provision of dynamic determinations of channel distortion level and external hindrances and noises.


Fig. GritTec Speaker-ID: The mobile client.

It allows to compensate channel distortion and influences of external hindrances with comparing spectra features, put into the original speech signal. Sensitivity to identifications is defined by the level of installing the thresholds of probability of errors 1-th (False Rejection Rate (FRR)) and 2-th (False Acceptance Rate (FAR)) sort. Possibility of regulation of thresholds of FRR and FAR allows to adjust a process of identification flexibly in accordance with system safety requirements.
  At the moment the GritTec's Speaker-ID engine are realized in software solution of voice identification with GUI interface - GritTec Speaker-ID: The mobile client.

Applications
  • For automatic voice identification or verification of unknown voice by phonogram of telephone negotiations;
  • In systems with high safety level, for instance, when access to digital information is limited by circle of given persons;
  • Applications where it's necessary to identify a person using peculiarities of his voice.

Features
  • Operation with low SNR;
  • Fast adaptation to changing of channel distortion and external noises;
  • Minimum duration of a speech signal with a voice example used for correct reception of voice parameters for the target speaker - not less 15 seconds;
  • Minimum duration of a speech signal with a voice example used for voice identification or voice verification - not less 7 seconds;
  • Speaker identification reliability not less than 90% if both of speech signals were recorded in the same channel;
  • Speaker identification reliability not less then 85% if both of speech signals were recorded in different channels (cross channels);
  • Supporting voice identification or voice verification in multi-threading mode;
  • Automatic voice identification or voice verification doesn't require special skills;
  • Easy integration with target applications.
 
Signal requirement
  • Signal format: 16-bits linear;
  • 8 kHz sampling rate;
  • SNR, at least 10 db;
  • Frequency range: 300-3400 Hz or better.

Availability
  • PC demo of voice identification engine in console window for MS Windows;
  • The software solution of voice identification on the base of GritTec Speaker-ID: The mobile client;
  • SDK for Intel x86, x64 platforms with object code or ANSI C++ float point code is available on request.

Achievements

For an estimation accuracy of GritTec's Speaker-ID engine in mode of voice verification it was used voices of 25 target speakers (12 - males, 13 - females) for English language. Each target speaker was trained separately for CELL and VOIP channel. It was used 50 files for training, 25 files - for CELL channel and 25 files - for VOIP channel. Each trained file contained 12 phrases of a random digit numbers (from 0 to 5) with the common duration ~ (40-50) seconds.

The total of files used for verification in CELL and VOIP channels was 31950, where 30195 files with voices of imposter speakers, and 1755 files with voices of target speakers. Each verified file contained 1 phrase of a random digit numbers (from 0 to 6) with the common duration ~ (4-6) seconds.

Screenshots of DET curves and EER (Equal Error Rate) errors of verification results for CELL and VOIP channels are shown below.

      EER: 5,96 % (training on CELL, verification on CELL);
      EER: 7.01 % (training on CELL, verification on CELL and VOIP);
      EER: 3.91 % (training on VOIP, verification on VOIP).
      EER: 8.11 % (training on VOIP, verification on CELL and VOIP);


All results have been received for GritTec's Speaker-ID (Version 2,90) with comparing Version 2,80.

For more information, please contact us via Online Request Form.