This paper introduces the use of two new features for speaker identification, Residual Phase Cepstrum Coefficients (RPCC) and Glottal Flow Cepstrum Coefficients (GLFCC), to capture speaker-specific characteristics from their vocal excitation patterns. Results on a cross-lingual speaker identification task taken from the NIST 2004 SRE demonstrate that these RPCC and GLFCC features are significantly more accurate than traditional mel-frequency cepstral coefficients (MFCC). In particular, these two new features give better results with smaller amounts of training data, due to lower model complexity. Index Terms — Speaker identification, Glottal sourc
The Capstrum coefficient features analysis plays a crucial role in the overall performance of the mu...
Mel-frequency cepstral coefficients (MFCCs) are widely adopted in speech recognition as well as spea...
The paper comparatively analyzes the speaker discrimination power of the vocal source and vocal trac...
This paper introduces the use of two new features for speaker identification, Residual Phase Cepstru...
Speaker identification and verification has received a great deal of attention from the speech commu...
This paper introduces the use of three physiologically-motivated features for speaker identification...
In this work, the significance of combining the evidence from multitaper mel-frequency cepstral coef...
We propose a novel feature set for speaker recognition that is based on the voice source signal. The...
Speaker recognition has received a great deal of attention from the speech community, and significan...
This paper describes a unique cross-phoneme speaker identification experiment, using deliberately mi...
A state of the art Speaker Identification (SI) system requires a robust feature extraction unit foll...
Based on the assumption that the physical characteristics of people's vocal apparatus cause their vo...
Speaker identification experiments are performed with novel features representative of the glottal s...
Speaker identification experiments are performed with novel features representative of the glottal s...
Spoken language identifcation (LID) in telephone speech signals is an important and difficult classi...
The Capstrum coefficient features analysis plays a crucial role in the overall performance of the mu...
Mel-frequency cepstral coefficients (MFCCs) are widely adopted in speech recognition as well as spea...
The paper comparatively analyzes the speaker discrimination power of the vocal source and vocal trac...
This paper introduces the use of two new features for speaker identification, Residual Phase Cepstru...
Speaker identification and verification has received a great deal of attention from the speech commu...
This paper introduces the use of three physiologically-motivated features for speaker identification...
In this work, the significance of combining the evidence from multitaper mel-frequency cepstral coef...
We propose a novel feature set for speaker recognition that is based on the voice source signal. The...
Speaker recognition has received a great deal of attention from the speech community, and significan...
This paper describes a unique cross-phoneme speaker identification experiment, using deliberately mi...
A state of the art Speaker Identification (SI) system requires a robust feature extraction unit foll...
Based on the assumption that the physical characteristics of people's vocal apparatus cause their vo...
Speaker identification experiments are performed with novel features representative of the glottal s...
Speaker identification experiments are performed with novel features representative of the glottal s...
Spoken language identifcation (LID) in telephone speech signals is an important and difficult classi...
The Capstrum coefficient features analysis plays a crucial role in the overall performance of the mu...
Mel-frequency cepstral coefficients (MFCCs) are widely adopted in speech recognition as well as spea...
The paper comparatively analyzes the speaker discrimination power of the vocal source and vocal trac...