A novel hybrid feature method based on Caelen auditory model and gammatone filterbank for robust speaker recognition under noisy environment and speech coding distortion

Currently, the majority of the state-of-the-art speaker recognition systems predominantly use short-term cepstral feature extraction approaches to parameterize the speech signals. In this paper, we propose new auditory features based Caelen auditory model that simulate the external, middle and inner parts of the ear and Gammtone filter for speaker recognition system, called Caelen Auditory Model Gammatone Cepstral Coefficients (CAMGTCC). The performances evaluations of the proposed feature are carried by the TIMIT and NIST 2008 corpus. The speech coding represent by Adaptive Multi-Rate wideband (AMR-WB) and noisy conditions using various noises SNR levels which are extracted from NOISEX-92. Speaker recognition system using GMM-UBM and i-vector-GPLDA modelling. The experimental results demonstrate that the proposed feature extraction method performs better compared to the Gammatone Cepstral Coefficients (GTCC) and Mel Frequency Cepstral Coefficients (MFCC) features. For speech coding distortion, the features extraction proposed improve the robustness of codec-degraded speech at different bit rates. In addition, when the test speech signals are corrupted with noise at SNRs ranging from (0 dB to 15 dB), we observe that CAMGTCC achieves overall equal error rate (EER) reduction of 10.88% to 6.8% relative, compared to baselines.

Automated summary

In this paper, a new auditory feature based on the Caelen auditory model and Gammtone filter (CAMGTCC) is proposed for speaker recognition system. The experimental results demonstrate that the proposed feature outperforms other feature extraction methods.