2018 International Joint Conference on Neural Networks (IJCNN) 2018
DOI: 10.1109/ijcnn.2018.8489497
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Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification

Abstract: The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise. To improve the robustness of such systems in unknown noisy environments, this paper proposes a simple pre-processing method called Noise Invariant Frame Selection (NIFS). Based on several noisy constraints, it selects noise invariant frames from utterances to represent speakers. Experiments conducted on the TIMIT database showed that the NIFS can significantly improve t… Show more

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Cited by 7 publications
(11 citation statements)
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“…In Section III, weighted GMM for i-vector extraction has been presented. In this section, we describe how to calculate different weights for different frames as shown in Fig.2 Inspired by [24], in our proposed algorithm, different types of noises are added to the original testing speech to explore the noise-robustness of different frames. In order to make straightforward comparison w.r.t.…”
Section: Weights Definition To Improve Noise Robustnessmentioning
confidence: 99%
See 3 more Smart Citations
“…In Section III, weighted GMM for i-vector extraction has been presented. In this section, we describe how to calculate different weights for different frames as shown in Fig.2 Inspired by [24], in our proposed algorithm, different types of noises are added to the original testing speech to explore the noise-robustness of different frames. In order to make straightforward comparison w.r.t.…”
Section: Weights Definition To Improve Noise Robustnessmentioning
confidence: 99%
“…In order to make straightforward comparison w.r.t. [24], in the following experiments, three types of noises, i.e. white, babble, and pink, are chosen the same as in [24].…”
Section: Weights Definition To Improve Noise Robustnessmentioning
confidence: 99%
See 2 more Smart Citations
“…To solve this problem, speech enhancement 4-6 and model adaptation approaches 7,8 have been widely used. Since the same noise may pose different distortion on different frames, by adopting several noisy constraints, Song et al 9 designed a simple framework that can select noise invariant frames from original audio signals. The experiment results show that this framework can enhance the speaker verification performance for different speaker models under different noisy conditions.…”
Section: Introductionmentioning
confidence: 99%