11/4/2022 0 Comments Catt acoustic v8![]() ![]() The harming part of room acoustics in automatic speech recognition. Petrick, R., Lohde, K., Wolff, M., & Hoffmann, R. IEEE Transactions on Audio, Speech, and Language Processing, 19, 196–205. Robust speaker recognition using denoised vocal source and vocal tract features. IEEE Transactions on Audio, Speech, and Language Processing, 15, 1711–1723. Robust speaker recognition in noisy conditions. Robust speaker recognition: A feature-based approach. In Fourth International Conference on Spoken Language. Increasing robustness in GMM speaker recognition systems for noisy and reverberant speech with low complexity microphone arrays. ![]() González-Rodríguez, J., Ortega-García, J., Martín, C., & Hernández, L. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (pp. #CATT ACOUSTIC V8 VERIFICATION#Feature normalization for speaker verification in room reverberation. In Tenth Annual Conference of the International Speech Communication Association. Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification. Springer.ĭehak, N., Dehak, R., Kenny, P., Brümmer, N., Ouellet, P., & Dumouchel, P. Combining SVMs with various feature selection strategies. In 2015 European Intelligence and Security Informatics Conference (EISIC) (pp. Improving robustness of speaker recognition in noisy and reverberant conditions via training. The Journal of the Acoustical Society of America, 65, 943–950.Īl-Noori, A. Image method for efficiently simulating small-room acoustics. ![]() International Journal of Speech Technology, 22(4), 1077–1084.Īllen, J. Early reflection detection using autocorrelation to improve robustness of speaker verification in reverberant conditions. In 2017 Seventh International Conference on Innovative Computing Technology (INTECH) (pp. Robust speaker verification in reverberant conditions using estimated acoustic parameters-A maximum likelihood estimation and training on the fly approach. International Journal of Information and Electronics Engineering, 5, 423.Īl-Karawi, K. Automatic speaker recognition system in adverse conditions-Implication of noise and reverberation on system performance. International Journal of Sensors, Wireless Communications and Control, 9, 1–10.Īl-Karawi, K. Robustness speaker recognition based on feature space in clean and noisy condition. Speaker verification experiments in the artificial and real reverberant conditions show the efficiency of the proposed methods in terms of decreased equal error rate EER and detection error trade-off DET.Īl-Karawi, K. While the second method is using multi training to combat the reverberation effect. The first method is using GFCC features as a robust feature to alleviate the effect of reverberation on system performance. This paper proposed two methods to combat the effect of reverberation on speaker verification performance. ![]() Recent research indicates that a new speaker feature, gammatone frequency cepstral coefficients (GFCC), exhibits superior noise and reverberation robustness than other features. the removal or reduction or other cleaning methods of the channel effects, to some extent, mitigates the mismatching problem at the cost of added distortions to the vulnerable speech signal themselves, and therefore, its effectiveness is limited. Speech signals recorded in far-field or with a far receiver typically comprise additive noise and reverberation, which cause degradation and distortion in the reliability and intelligibility of speech signal, and the recognition performance of speaker recognition systems, with severe consequences in a wide range of real applications. ![]()
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