Audio enhancing with DNN autoencoder for speaker recognition

Plchot, Oldřich; Burget, Lukáš; Aronowitz, Hagai; Matějka, Pavel

doi:10.1109/icassp.2016.7472647

Cited by 56 publications

(29 citation statements)

References 17 publications

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“…To compensate for these adverse impacts, various approaches have been proposed at different stages of the ASV systems. At the signal level, DNN based speech or feature enhancement [4,5,6,7] has been investigated for ASV under complex environment. At the feature level, feature normalization techniques [8] and noise-robust features such as power-normalized cepstral coefficients (PNCC) [9] have also been applied to ASV systems.…”

Section: Introductionmentioning

confidence: 99%

Within-Sample Variability-Invariant Loss for Robust Speaker Recognition Under Noisy Environments

Cai

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Despite the significant improvements in speaker recognition enabled by deep neural networks, unsatisfactory performance persists under noisy environments. In this paper, we train the speaker embedding network to learn the "clean" embedding of the noisy utterance. Specifically, the network is trained with the original speaker identification loss with an auxiliary within-sample variability-invariant loss. This auxiliary variability-invariant loss is used to learn the same embedding among the clean utterance and its noisy copies and prevents the network from encoding the undesired noises or variabilities into the speaker representation. Furthermore, we investigate the data preparation strategy for generating clean and noisy utterance pairs on-the-fly. The strategy generates different noisy copies for the same clean utterance at each training step, helping the speaker embedding network generalize better under noisy environments. Experiments on VoxCeleb1 indicate that the proposed training framework improves the performance of the speaker verification system in both clean and noisy conditions.

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Section: Introductionmentioning

confidence: 99%

Within-Sample Variability-Invariant Loss for Robust Speaker Recognition Under Noisy Environments

Cai

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Future work will involve the automation of such manual processes, perhaps through the use of speaker diarization and/or audio enhancement (e.g. see [31]), as well as the expansion of the types of issues addressed in the system such as shortduration modeling [32] and signal quantization issues.…”

Section: Error Analysismentioning

confidence: 99%

The IBM Speaker Recognition System: Recent Advances and Error Analysis

2016

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We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech. Some of the key advancements that contribute to our system include: a nearest-neighbor discriminant analysis (NDA) approach (as opposed to LDA) for intersession variability compensation in the i-vector space, the application of speaker and channel-adapted features derived from an automatic speech recognition (ASR) system for speaker recognition, and the use of a DNN acoustic model with a very large number of output units (∼10k senones) to compute the frame-level soft alignments required in the i-vector estimation process. We evaluate these techniques on the NIST 2010 SRE extended core conditions (C1-C9), as well as the 10sec-10sec condition. To our knowledge, results achieved by our system represent the best performances published to date on these conditions. For example, on the extended tel-tel condition (C5) the system achieves an EER of 0.59%. To garner further understanding of the remaining errors (on C5), we examine the recordings associated with the low scoring target trials, where various issues are identified for the problematic recordings/trials. Interestingly, it is observed that correcting the pathological recordings not only improves the scores for the target trials but also for the nontarget trials.

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“…A common way to improve the robustness of the system is to train the system using a dataset consisting of clean and noisy data [14]. Speech enhancement is another way of denoising such as short-time spectral amplitude minimum mean square error (STSA-MMSE) [15] and many DNN-based enhancement methods [16,17,18]. Unlike previous works denoising in the front-end, we plan to use a multi-task adversarial framework to extract the noise-robust speaker representation directly.…”

Section: Introductionmentioning

confidence: 99%

Training Multi-task Adversarial Network for Extracting Noise-robust Speaker Embedding

Zhou

Jiang

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Under noisy environments, to achieve the robust performance of speaker recognition is still a challenging task. Motivated by the promising performance of multi-task training in a variety of image processing tasks, we explore the potential of multitask adversarial training for learning a noise-robust speaker embedding. In this paper, we present a novel framework that consists of three components: an encoder that extracts the noise-robust speaker embeddings; a classifier that classifies the speakers; a discriminator that discriminates the noise type of the speaker embeddings. Additionally , we propose a training strategy using the training accuracy as an indicator to stabilize the multi-class adversarial optimization process. We conduct our experiments on the English and Mandarin corpuses and the experimental results demonstrate that our proposed multi-task adversarial training method could greatly outperform the other methods without adversarial training in noisy environments. Furthermore, the experiments indicate that our method is also able to improve the speaker verification performance under the clean condition.

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Audio enhancing with DNN autoencoder for speaker recognition

Cited by 56 publications

References 17 publications

Within-Sample Variability-Invariant Loss for Robust Speaker Recognition Under Noisy Environments

Within-Sample Variability-Invariant Loss for Robust Speaker Recognition Under Noisy Environments

The IBM Speaker Recognition System: Recent Advances and Error Analysis

Training Multi-task Adversarial Network for Extracting Noise-robust Speaker Embedding

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