3,130 research outputs found
Robust Speaker Recognition Using Speech Enhancement And Attention Model
In this paper, a novel architecture for speaker recognition is proposed by
cascading speech enhancement and speaker processing. Its aim is to improve
speaker recognition performance when speech signals are corrupted by noise.
Instead of individually processing speech enhancement and speaker recognition,
the two modules are integrated into one framework by a joint optimisation using
deep neural networks. Furthermore, to increase robustness against noise, a
multi-stage attention mechanism is employed to highlight the speaker related
features learned from context information in time and frequency domain. To
evaluate speaker identification and verification performance of the proposed
approach, we test it on the dataset of VoxCeleb1, one of mostly used benchmark
datasets. Moreover, the robustness of our proposed approach is also tested on
VoxCeleb1 data when being corrupted by three types of interferences, general
noise, music, and babble, at different signal-to-noise ratio (SNR) levels. The
obtained results show that the proposed approach using speech enhancement and
multi-stage attention models outperforms two strong baselines not using them in
most acoustic conditions in our experiments.Comment: Acceptted by Odyssey 202
Speech Enhancement By Exploiting The Baseband Phase Structure Of Voiced Speech For Effective Non-Stationary Noise Estimation
Speech enhancement is one of the most important and challenging issues in the speech communication and signal processing field. It aims to minimize the effect of additive noise on the quality and intelligibility of the speech signal. Speech quality is the measure of noise remaining after the processing on the speech signal and of how pleasant the resulting speech sounds, while intelligibility refers to the accuracy of understanding speech. Speech enhancement algorithms are designed to remove the additive noise with minimum speech distortion.The task of speech enhancement is challenging due to lack of knowledge about the corrupting noise. Hence, the most challenging task is to estimate the noise which degrades the speech. Several approaches has been adopted for noise estimation which mainly fall under two categories: single channel algorithms and multiple channel algorithms. Due to this, the speech enhancement algorithms are also broadly classified as single and multiple channel enhancement algorithms.In this thesis, speech enhancement is studied in acoustic and modulation domains along with both amplitude and phase enhancement. We propose a noise estimation technique based on the spectral sparsity, detected by using the harmonic property of voiced segment of the speech. We estimate the frame to frame phase difference for the clean speech from available corrupted speech. This estimated frame-to-frame phase difference is used as a means of detecting the noise-only frequency bins even in voiced frames. This gives better noise estimation for the highly non-stationary noises like babble, restaurant and subway noise. This noise estimation along with the phase difference as an additional prior is used to extend the standard spectral subtraction algorithm. We also verify the effectiveness of this noise estimation technique when used with the Minimum Mean Squared Error Short Time Spectral Amplitude Estimator (MMSE STSA) speech enhancement algorithm. The combination of MMSE STSA and spectral subtraction results in further improvement of speech quality
Speaker Re-identification with Speaker Dependent Speech Enhancement
While the use of deep neural networks has significantly boosted speaker
recognition performance, it is still challenging to separate speakers in poor
acoustic environments. Here speech enhancement methods have traditionally
allowed improved performance. The recent works have shown that adapting speech
enhancement can lead to further gains. This paper introduces a novel approach
that cascades speech enhancement and speaker recognition. In the first step, a
speaker embedding vector is generated , which is used in the second step to
enhance the speech quality and re-identify the speakers. Models are trained in
an integrated framework with joint optimisation. The proposed approach is
evaluated using the Voxceleb1 dataset, which aims to assess speaker recognition
in real world situations. In addition three types of noise at different
signal-noise-ratios were added for this work. The obtained results show that
the proposed approach using speaker dependent speech enhancement can yield
better speaker recognition and speech enhancement performances than two
baselines in various noise conditions.Comment: Acceptted for presentation at Interspeech202
Acoustic Echo and Noise Cancellation System for Hand-Free Telecommunication using Variable Step Size Algorithms
In this paper, acoustic echo cancellation with doubletalk detection system is implemented for a hand-free telecommunication system using Matlab. Here adaptive noise canceller with blind source separation (ANC-BSS) system is proposed to remove both background noise and far-end speaker echo signal in presence of double-talk. During the absence of double-talk, far-end speaker echo signal is cancelled by adaptive echo canceller. Both adaptive noise canceller and adaptive echo canceller are implemented using LMS, NLMS, VSLMS and VSNLMS algorithms. The normalized cross-correlation method is used for double-talk detection. VSNLMS has shown its superiority over all other algorithms both for double-talk and in absence of double-talk. During the absence of double-talk it shows its superiority in terms of increment in ERLE and decrement in misalignment. In presence of double-talk, it shows improvement in SNR of near-end speaker signal
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