Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi- faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. To tackle the problem, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM jointly learns masked speech prediction and denoising in pre-training. By this means, WavLM does not only keep the speech content modeling capability by the masked speech prediction, but also improves the potential to non-ASR tasks by the speech denoising. In addition, WavLM employs gated relative position bias for the Transformer structure to better capture the sequence ordering of input speech. We also scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. The code and pre- trained models are available at https://aka.ms/wavlm.

Voice and face are two most popular biometrics for person verification, usually used in speaker verification and face verification tasks. It has already been observed that simply combining the information from these two modalities can lead to a more powerful and robust person verification system. In this article, to fully explore the multi-modal learning strategies for person verification, we proposed three types of audio-visual deep neural network (AVN), including feature level AVN (AVN-F), embedding level AVN (AVN-E), and embedding level combination with joint learning AVN (AVN-J). To further enhance the system robustness in real noisy conditions where not both modalities can be accessed with high-quality, we proposed several data augmentation strategies for each proposed AVN: A feature-level multi-modal data augmentation is proposed for AVN-F and an embedding-level data augmentation with novel noise distribution matching is designed for AVN-E. For AVN-J, both the feature and embedding level multi-modal data augmentation methods can be applied. All the proposed models are trained on the VoxCeleb2 dev dataset and evaluated on the standard VoxCeleb1 dataset, and the best system achieves 0.558, 0.441% and 0.793% EER on the three official trial lists of VoxCeleb1, which is to our knowledge the best published single system results on this corpus for person verification. To validate the robustness of the proposed approaches, a noisy evaluation set based on the VoxCeleb1 is constructed, and experimental results show that the proposed system can significantly boost the system robustness and still show promising performance under this noisy scenario.

The cocktail party problem, i.e., tracing and recognizing the speech of a specific speaker when multiple speakers talk simultaneously, is one of the critical problems yet to be solved to enable the...

Data augmentation is an effective method to improve the robustness of embedding based speaker verification systems, which could be applied to either the front-end speaker embedding extractor or the back-end PLDA. Different from the conventional augmentation methods such as manually adding noise or reverberation to the original audios, in this paper, we propose to use deep generative models to directly generate more diverse speaker embeddings, which would be used for robust PLDA training. Conditional GAN and VAE are designed and investigated for different embedding types, including factor analysis based i-vector, TDNN based x-vector and ResNet based r-vector. The proposed methods are evaluated on NIST SRE 2016 and 2018 dataset. Within the popular x-vector and r-vector framework, the experimental results show that our proposed methods can outperform the traditional audio based back-end augmentation method while different front-end augmentation methods are considered.

Although great progress has been made in automatic speech recognition, significant performance degradation still exists in noisy environments. Our previous work has demonstrated the superior noise robustness of very deep convolutional neural networks (VDCNN). Based on our work on VDCNNs, this paper proposes a more advanced model referred to as the very deep convolutional residual network (VDCRN). This new model incorporates batch normalization and residual learning, showing more robustness than previous VDCNNs.Then, to alleviate the mismatch between the training and testing conditions, model adaptation and adaptive training are developed and compared for the new VDCRN. This paper focuses on factor aware training (FAT) and cluster adaptive training (CAT). For FAT, a unified framework is explored. For CAT, two schemes are first explored to construct the bases in the canonical model; furthermore, a factorized version of CAT is designed to address multiple nonspeech variabilities in one model. Finally, a complete multipass system is proposed to achieve the best system performance in the noisy scenarios. The proposed new approaches are evaluated on three different tasks: Aurora4 (simulated data with additive noise and channel distortion), CHiME4 (both simulated and real data with additive noise and reverberation), and the AMI meeting transcription task (real data with significant reverberation).The evaluation not only includes different noisy conditions, but also covers both simulated and real noisy data. The experiments show that the new VDCRN is more robust, and the adaptation on this model can further significantly reduce the word error rate (WER). The proposed best architecture obtains consistent and very large improvements on all tasks compared to the baseline VDCNN or long short-term memory. Particularly, on Aurora4 a new milestone 5.67% WER is achieved by only improving acoustic modeling.

Most of the existing feature representations for spoofing countermeasures consider information either from the magnitude or phase spectrum. We hypothesize that both magnitude and phase spectra can be beneficial for spoofing detection (SD) when collectively used to capture the signal artifacts. In this work, we propose a novel feature referred to as modified magnitude-phase spectrum (MMPS) to capture both magnitude and phase information from the speech signal. The constant-Q transform is used to obtain the magnitude and phase information in terms of MMPS, which can be denoted as CQT-MMPS. We then use this information for the proposal of a handcrafted feature, namely, constant-Q modified octave coefficients (CQMOC). To evaluate the proposed CQT-MMPS and CQMOC features, three classic anti-spoofing models are adopted, including the Gaussian mixture model (GMM), the light CNN (LCNN) and the ResNet. Additionally, since there is usually no prior knowledge about the spoofing kind in real-world applications, two novel methods referred to as three-class classifiers with maximum spoofing-score (TCMS) and multi-task learning (MTL) are designed for unknown-kind SD (UKSD). The experimental results on ASVspoof 2019 corpus show that CQMOC outperforms most of the commonly-used handcrafted features, and the CQT-based MMPS performs better than the magnitude-phase spectrum and the commonly-used log power spectrum. Further, the MMPS-based systems can achieve comparable or even better performance when compared with the state-of-the-art systems. We find that the newly-designed TCMS and MTL methods outperform the combination-based method for UKSD and meanwhile, generalize much better than the respective-kind-based methods in cross-spoofing-kind evaluation scenarios.