Speaker Recognition using Supra-segmental Level Excitation Information

Speaker specific information present in the excitation signal is mostly viewed from sub-segmental, segmental and supra-segmental levels. In this work, the supra-segmental level information is explored for recognizing speakers. Earlier study has shown that, combined use of pitch and epoch strength vectors provides useful supra-segmental information. However, the speaker recognition accuracy achieved by supra-segmental level feature is relatively poor than other levels source information. May be the modulation information present at the supra-segmental level of the excitation signal is not manifested properly in pith and epoch strength vectors. We propose a method to model the supra-segmental level modulation information from residual mel frequency cepstral coefficient (R-MFCC) trajectories. The evidences from R-MFCC trajectories combined with pitch and epoch strength vectors are proposed to represent supra-segmental information. Experimental results show that compared to pitch and epoch strength vectors, the proposed approach provides relatively improved performance. Further, the proposed supra-segmental level information is relatively more complimentary to other levels information

Limited Data Speaker Verification: Fusion of Features

The present work demonstrates experimental evaluation of speaker verification for different speech feature extraction techniques with the constraints of limited data (less than 15 seconds). The state-of-the-art speaker verification techniques provide good performance for sufficient data (greater than 1 minutes). It is a challenging task to develop techniques which perform well for speaker verification under limited data condition. In this work different features like Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPCC), Delta (4), Delta-Delta (44), Linear Prediction Residual (LPR) and Linear Prediction Residual Phase (LPRP) are considered. The performance of individual features is studied and for better verification performance, combination of these features is attempted. A comparative study is made between Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM) through experimental evaluation. The experiments are conducted using NIST-2003 database. The experimental results show that, the combination of features provides better performance compared to the individual features. Further GMM-UBM modeling gives reduced equal error rate (EER) as compared to GMM

The Effect Of Acoustic Variability On Automatic Speaker Recognition Systems

This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre-forensic use. The thesis begins with a literature review and explanation of relevant technical terms. Five categories of research experiments then examine ASR performance, reflective of conditions influencing speech quantity (inhibitors) and speech quality (contaminants), acknowledging quality often influences quantity. Experiments pertain to: net speech duration, signal to noise ratio (SNR), reverberation, frequency bandwidth and transcoding (codecs). The ASR system is placed under scrutiny with examination of settings and optimum conditions (e.g. matched/unmatched test audio and speaker models). Output is examined in relation to baseline performance and metrics assist in informing if ASRs should be applied to suboptimal audio recordings. Results indicate that modern ASRs are relatively resilient to low and moderate levels of the acoustic contaminants and inhibitors examined, whilst remaining sensitive to higher levels. The thesis provides discussion on issues such as the complexity and fragility of the speech signal path, speaker variability, difficulty in measuring conditions and mitigation (thresholds and settings). The application of ASRs to casework is discussed with recommendations, acknowledging the different modes of operation (e.g. investigative usage) and current UK limitations regarding presenting ASR output as evidence in criminal trials. In summary, and in the context of acoustic variability, the thesis recommends that ASRs could be applied to pre-forensic cases, accepting extraneous issues endure which require governance such as validation of method (ASR standardisation) and population data selection. However, ASRs remain unsuitable for broad forensic application with many acoustic conditions causing irrecoverable speech data loss contributing to high error rates