Authors:
Giorgio Biagetti
;
Paolo Crippa
;
Alessandro Curzi
;
Simone Orcioni
and
Claudio Turchetti
Affiliation:
Università Politecnica delle Marche, Italy
Keyword(s):
Speaker Identification, Speaker Recognition, Classification, Speech, Speech Frames, Short Sequences, DKLT, GMM, EM Algorithm, MFCC, Cepstral Analysis, Feature Extraction, Digitized Voice Samples.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Audio and Speech Processing
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Digital Signal Processing
;
Health Engineering and Technology Applications
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Telecommunications
Abstract:
In biometric person identification systems, speaker identification plays a crucial role as the voice is the more natural signal to produce and the simplest to acquire. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to de-correlate the vocal source and the vocal tract filter make them suitable for speech recognition, they show up some drawbacks in speaker recognition. This paper presents an experimental evaluation showing that reducing the dimension of features by using the discrete Karhunen-Loève transform (DKLT), guarantees better performance with respect to conventional MFCC features. In particular with short sequences of speech frames, that is with utterance duration of less than 1 s, the performance of truncated DKLT representation are always better than MFCC.