Abstract
This paper presents the fundamental part of all automatic speaker recognition systems (ASR) which is namely partem recognition used to measure similarity between speaker model stored in a system and parameters extracted from the test utterance of an identified speaker. The fundamentals of the most commonly applied methods like long term statistics, vector quantization (VQ) and nearest neighbour method (NN) are included. An implementation of a vector quantization in a constructed speaker recognition system in a Matlab environment is shown and obtained results are discussed. The influence of several different speech parameters extracted from speaker utterances on identification accuracy is also included. Identification was done on Polish speech corpora ROBOT.
| Original language | English |
|---|---|
| Pages (from-to) | 235-240 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 36 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2003 |
| Event | 6th IFAC Workshop on Programmable Devices and Systems, PDS 2003 - Ostrava, Czech Republic Duration: 11 Feb 2003 → 13 Feb 2003 |
Keywords
- Pattern recognition
- Signal processing
- Speaker identification
- Speaker verification
- Speech analysis
ASJC Scopus subject areas
- Control and Systems Engineering
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