Speaker diarization is typically considered as a discriminative task, using discriminative approaches to produce fixed diarization results. In this paper, we explore for the first time the use of neural network-based generative methods for speaker diarization. We implement a Flow-Matching (FM) based generative algorithm within the sequenceto-sequence target speaker voice activity detection (Seq2Seq-TSVAD) diarization system. Our experiments reveal that applying the generative method directly to the original binary label sequence space of the TS-VAD output is ineffective. To address this issue, we propose mapping the binary label sequence into a dense latent space before applying the generative algorithm, and our proposed Flow-TSVAD method can significantly outperform the traditional Seq2Seq-TSVAD system. Additionally, we observe that the FM algorithm converges rapidly during the inference stage, only requiring two inference steps to achieve promising results. Moreover, as a generative model, Flow-TSVAD allows for sampling different diarization results by running the model multiple times, so the ensemble system combining the results from various sampling instances can further boost the diarization performance.

The goal of the acoustic scene classification (ASC) task is to classify recordings into one of the predefined acoustic scene classes. However, in real-world scenarios, ASC systems often encounter challenges such as recording device mismatch, low-complexity constraints, and the limited availability of labeled data. To alleviate these issues, in this paper, a data-efficient and low-complexity ASC system is built with a new model architecture and better training strategies. Specifically, we firstly design a new low-complexity architecture named Rep-Mobile by integrating multi-convolution branches which can be reparameterized at inference. Compared to other models, it achieves better performance and less computational complexity. Then we apply the knowledge distillation strategy and provide a comparison of the data efficiency of the teacher model with different architectures. Finally, we propose a progressive pruning strategy, which involves pruning the model multiple times in small amounts, resulting in better performance compared to a single step pruning. Experiments are conducted on the TAU dataset. With Rep-Mobile and these training strategies, our proposed ASC system achieves the state-of-the-art (SOTA) results so far, while also winning the first place with a significant advantage over others in the DCASE2024 Challenge.

The acoustic background plays a crucial role in natural conversation. It provides context and helps listeners understand the environment, but a strong background makes it difficult for listeners to understand spoken words. The appropriate handling of these backgrounds is situation-dependent: Although it may be necessary to remove background to ensure speech clarity, preserving the background is sometimes crucial to maintaining the contextual integrity of the speech. Despite recent advancements in zero-shot Text-to-Speech technologies, current systems often struggle with speech prompts containing backgrounds. To address these challenges, we propose a Controllable Masked Speech Prediction strategy coupled with a dual-speaker encoder, utilizing a task-related control signal to guide the prediction of dual background removal and preservation targets. Experimental results demonstrate that our approach enables precise control over the removal or preservation of background across various acoustic conditions and exhibits strong generalization capabilities in unseen scenarios.

Machine anomalous sound detection is a useful technique for various applications, but it often suffers from poor generalization due to the challenges of data collection and complex acoustic environment. To address this issue, we propose a robust machine anomalous sound detection model that leverages self-supervised pre-trained models on large-scale speech data. Specifically, we assign different weights to the features from different layers of the pre-trained model and then use the working condition as the label for self-supervised classification fine-tuning. Moreover, we introduce a data augmentation method that simulates different operating states of the machine to enrich the dataset. Furthermore, we devise a transformer pooling method that fuses the features of different segments. Experiments on the DCASE2023 dataset show that our proposed method outperforms the commonly used reconstruction-based autoencoder and classification-based convolutional network by a large margin, demonstrating the effectiveness of large-scale pre-training for enhancing the generalization and robustness of machine anomalous sound detection. In Task2 of DCASE2023, we achieve 2nd place with these methods.

Speaker verification encounters significant challenges when confronted with diverse domain data, often resulting in performance degradation due to domain mismatch. To enhance performance in cross-domain scenarios, we introduce the Domain Adapter, an adaptable module designed for specific domains. This module learns and integrates domain-specific information with speaker-related data, mitigating domain-related variations and promoting convergence of utterance embeddings from the same speaker across diverse domains. It offers configurability across multiple levels and is adaptable to various backbone architectures. Our proposed module substantially enhances cross-domain performance with minimal parameter increments while effectively generalizing to previously unseen domains. In our experiments, we present results on the 3D-Speaker dataset, which provides acoustically-relevant attributes crucial for domain categorization and the subsequent learning of domain information. The top-performing system integrated with domain adapters achieved 10.8%, 14.8%, and 21.1% EER improvements over the baseline across three 3D-Speaker dataset trials.

We introduce a novel task named ‘target speech diarization’, which seeks to determine ‘when target event occurred’ within an audio signal. We devise a neural architecture called Prompt-driven Target Speech Diarization (PTSD), that works with diverse prompts that specify the target speech events of interest. We train and evaluate PTSD using sim2spk, sim3spk and sim4spk datasets, which are derived from the Librispeech. We show that the proposed framework accurately localizes target speech events. Furthermore, our framework exhibits versatility through its impressive performance in three diarization-related tasks: target speaker voice activity detection, overlapped speech detection and gender diarization. In particular, PTSD achieves comparable performance to specialized models across these tasks on both real and simulated data. This work serves as a reference benchmark and provides valuable insights into prompt-driven target speech processing.

Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets. To boost the system performance, researchers leverage large pretrained models such as WavLM to transfer learned high-level features to the downstream speaker recognition task. However, this approach introduces extra parameters as the pretrained model remains in the inference stage. Another group of researchers directly apply self-supervised methods such as DINO to speaker embedding learning, yet they have not explored its potential on large-scale in-the-wild datasets. In this paper, we present the effectiveness of DINO training on the large-scale WenetSpeech dataset and its transferability in enhancing the supervised system performance on the CNCeleb dataset. Additionally, we introduce a confidence-based data filtering algorithm to remove unreliable data from the pretraining dataset, leading to better performance with less training data. The associated pretrained models, confidence files, pretraining and finetuning scripts will be made available in the Wespeaker toolkit.

Voice conversion (VC) aims to modify the speaker’s timbre while retaining speech content. Previous approaches have tokenized the outputs from self-supervised into semantic tokens, facilitating disentanglement of speech content information. Recently, in-context learning (ICL) has emerged in text-to-speech (TTS) systems for effectively modeling specific characteristics such as timbre through context conditioning. This paper proposes an ICL capability enhanced VC system (ICL-VC) employing a mask and reconstruction training strategy based on flow-matching generative models. Augmented with semantic tokens, our experiments on the LibriTTS dataset demonstrate that ICL-VC improves speaker similarity. Additionally, we find that k-means is a versatile tokenization method applicable to various pre-trained models. However, the ICL-VC system faces challenges in preserving the prosody of the source speech. To mitigate this issue, we propose incorporating prosody embeddings extracted from a pre-trained emotion recognition model into our system. Integration of prosody embeddings notably enhances the system’s capability to preserve source speech prosody, as validated on the Emotional Speech Database.

Deep neural network-based systems have significantly improved the performance of speaker diarization tasks. However, end-to-end neural diarization (EEND) systems often struggle to generalize to scenarios with an unseen number of speakers, while target speaker voice activity detection (TS-VAD) systems tend to be overly complex. In this paper, we propose a simple attention-based encoder-decoder network for end-to-end neural diarization (AED-EEND). In our training process, we introduce a teacher-forcing strategy to address the speaker permutation problem, leading to faster model convergence. For evaluation, we propose an iterative decoding method that outputs diarization results for each speaker sequentially. Additionally, we propose an Enhancer module to enhance the frame-level speaker embeddings, enabling the model to handle scenarios with an unseen number of speakers. We also explore replacing the transformer encoder with a Conformer architecture, which better models local information. Furthermore, we discovered that commonly used simulation datasets for speaker diarization have a much higher overlap ratio compared to real data. We found that using simulated training data that is more consistent with real data can achieve an improvement in consistency. Extensive experimental validation demonstrates the effectiveness of our proposed methodologies. Our best system achieved a new state-of-the-art diarization error rate (DER) performance on all the CALLHOME (10.08%), DIHARD II (24.64%), and AMI (13.00%) evaluation benchmarks when overlap is considered and no oracle voice activity detection (VAD) is used. Beyond speaker diarization, our AED-EEND system also shows remarkable competitiveness as a speech type detection model.

The automatic speaker verification task has achieved great success using deep learning approaches with a large-scale, manually annotated dataset. However, collecting a significant amount of well-labeled data for system building is very difficult and expensive. Recently, self-supervised speaker verification has attracted a lot of interest due to its no dependency on labeled data. In this article, we propose a novel and advanced self-supervised learning framework based on our prior work, which can construct a powerful speaker verification system with high performance without using any labeled data. To avoid the impact of false negative pairs, we adopt the self-distillation with no labels (DINO) framework as the initial model, which can be trained without exploiting negative pairs. Then, we further introduce a cluster-aware training strategy for DINO to improve the diversity of data. In the iterative learning stage, due to a mass of unreliable labels from unsupervised clustering, the quality of pseudo labels is important for the system performance. This motivates us to propose dynamic loss-gate and label correction (DLG-LC) methods to alleviate the performance degradation caused by unreliable labels. Furthermore, we extend the DLG-LC from single-modality to multi-modality on the audio-visual dataset to further improve the performance. The experiments were conducted using the widely-used Voxceleb dataset. Compared to the best-known self-supervised speaker verification system, our proposed method achieve relative EER improvement of 22.17%, 27.94% and 25.56% on Vox-O, Vox-E and Vox-H test sets, even with fewer iterations, smaller models, and simpler clustering methods. Importantly, the newly proposed self-supervised learning system even achieves comparable results with the fully supervised system, but without using any human-labeled data.

Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such as speaker recognition, speaker diarization, speech synthesis, and target speaker extraction. In this overview, we present a comprehensive review of neural approaches to speaker representation learning from both theoretical and practical perspectives. Theoretically, we discuss speaker encoders ranging from supervised to self-supervised learning algorithms, standalone models to large pretrained models, pure speaker embedding learning to joint optimization with downstream tasks, and efforts toward interpretability. Practically, we systematically examine approaches for robustness and effectiveness, introduce and compare various open-source toolkits in the field. Through the systematic and comprehensive review of the relevant literature, research activities, and resources, we provide a clear reference for researchers in the speaker characterization and modeling field, as well as for those who wish to apply speaker modeling techniques to specific downstream tasks.

The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task. For existing loss functions, metric learning-based objectives depend strongly on searching effective pairs which might hinder further improvements. And popular multi-classification methods are usually observed with degradation when evaluated on unseen speakers. In this work, we introduce SphereFace2 framework which uses several binary classifiers to train the speaker model in a pair-wise manner instead of performing multi-classification. Benefiting from this learning paradigm, it can efficiently alleviate the gap between training and evaluation. Experiments conducted on Voxceleb show that the SphereFace2 outperforms other existing loss functions, especially on hard trials. Besides, large margin fine-tuning strategy is proven to be compatible with it for further improvements. Finally, SphereFace2 also shows its strong robustness to class-wise noisy labels which has the potential to be applied in the semi-supervised training scenario with inaccurate estimated pseudo labels.

This paper presents the design and implementation of our system for Track 1 of the Multi-modal Information based Speech Processing (MISP) 2022 Challenge. We design an end-to-end transformer-based multi-talker system. The transformer backbone is well-suited to capture long-term features, which is crucial for multi-modal speaker diarization in cases where temporal modalities are missing. Besides, we employ several loss functions and image data augmentation techniques to prevent over-fitting during training. Moreover, to further improve the system’s performance, we incorporate Interchannel Phase Difference (IPD) to model the location features and pre-train an ECAPA-TDNN-based model to extract speaker embedding features. Our system achieved a diarization error rate (DER) of 10.82% on the evaluation set, which earned us second place in the audio-visual speaker diarization task of the MISP 2022 challenge.

Speaker modeling is essential for many related tasks, such as speaker recognition and speaker diarization. The dominant modeling approach is fixed-dimensional vector representation, i.e., speaker embedding. This paper introduces a research and production oriented speaker embedding learning toolkit, Wespeaker. Wespeaker contains the implementation of scalable data management, state-of-the-art speaker embedding models, loss functions, and scoring back-ends, with highly competitive results achieved by structured recipes which were adopted in the winning systems in several speaker verification challenges. The application to other downstream tasks such as speaker diarization is also exhibited in the related recipe. Moreover, CPU- and GPU-compatible deployment codes are integrated for production-oriented development. The toolkit is publicly available at https://github.com/wenet-e2e/wespeaker.

Large margin fine-tuning (LMFT) is an effective strategy to improve the speaker verification system’s performance and is widely used in speaker verification challenge systems. Because the large margin in the loss function could make the training task too difficult, people usually use longer training segments to alleviate this problem in LMFT. However, the LMFT model could have a duration mismatch with the real scenario verification, where the verification speech may be very short. In our experiments, we also find that LMFT fails in short duration and other verification scenarios. To solve this problem, we propose the duration-based and similarity-based adaptive large margin fine-tuning (ALMFT) strategy. To verify its effectiveness, we constructed fixed, variable length, and asymmetric verification trials based on VoxCeleb1. Experimental results demonstrate that ALMFT algorithms are very effective and robust, which not only achieve comparable improvement with LMFT in official VoxCeleb evaluation trials but also overcome performance degradation problems in short-duration and asymmetric scenarios respectively.

Deep speaker embedding learning based on neural networks has become the predominant approach in speaker verification (SV) currently. In prior studies, researchers have investigated various network architectures. However, rare works pay attention to the question of how to design and scale up networks in a principled way to achieve a better trade-off on model performance and computational complexity. In this paper, we focus on efficient architecture design for speaker verification. Firstly, we systematically study the effect of the network depth and width on performance and empirically discover thatdepth is more important than the width of networks for speaker verification task. Based on this observation, we propose a novel depth-first (DF) architecture design rule. By applying it to ResNet and ECAPA-TDNN, two new families of much deeper models, namely DF-ResNets and DF-ECAPAs, are constructed. In addition, to further boost the performance of small models in the low computation regime, a novel attentive feature fusion (AFF) scheme is proposed to replace the conventional feature fusion methods. Specifically, we design two different fusion strategies, including sequential AFF (S-AFF) and parallel AFF (P-AFF), which can dynamically fuse features in a learnable way. Experimental results on the VoxCeleb dataset show that the newly proposed DF-ResNets and DF-ECAPAs can achieve a much better trade-off on performance and complexity than the original ResNet and ECAPA-TDNN. Moreover, small models can further obtain up to 40% relative improvement in EER by adopting AFF scheme with negligible computational cost. Finally, a comprehensive comparison with various other published SV systems illustrates that our proposed models achieve the best trade-off on performance and complexity in both low and high computation scenarios.

The speech representations learned from large-scale unlabeled data have shown better generalizability than those from supervised learning and thus attract a lot of interest to be applied for various downstream tasks. In this paper, we explore the limits of speech representations learned by different self-supervised objectives and datasets for automatic speaker verification (ASV), especially with a well-recognized SOTA ASV model, ECAPA-TDNN [1], as a downstream model. The representations from all hidden layers of the pre-trained model are firstly averaged with learnable weights and then fed into the ECAPA-TDNN as input features. The experimental results on Voxceleb dataset show that the weighted average representation is significantly superior to FBank, a conventional handcrafted feature for ASV. Our best single system achieves 0.537%, 0.569%, and 1.180% equal error rate (EER) on the three official trials of VoxCeleb1, separately. Accordingly, the ensemble system with three pre-trained models can further improve the EER to 0.479%, 0.536% and 1.023%. Among the three evaluation trials, our best system outperforms the winner system [2] of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC2021) on the VoxCeleb1-E trial.

Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years have witnessed great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply multi-task learning to the current SSL framework, where we integrate utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporated during training. We integrate the proposed methods into the HuBERT framework. Experiment results on the SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up the training dataset to 94 thousand hours of public audio data and achieve further performance improvement in all SUPERB tasks.

Convolution and self-attention based neural networks have both obtained excellent performance in automatic speaker verification. However, the convolution model often lacks the ability of long-term dependency modeling due to the limitation of receptive field, while the self-attention model is insufficient to model local information. To tackle this limitation, we propose a new multi-layer perceptrons based speaker verification network (MLP-SVNet) which can apply MLPs across temporal and frequency dimensions to capture the local and global information at the same time. The experimental results conducted on Voxceleb show that the proposed model is very competitive when compared to other systems based on convolution or self-attention. In addition, we demonstrate that MLP-SVNet based on multi-layer per-ceptrons can produce complementary embeddings, which can be fused with the state-of-the-art system to further improve the performance.

Transformer based on self attention mechanism has demonstrated its state-of-the-art performance in most natural language processing (NLP) tasks, but it’s not very competitive when applied for speaker verification in previous works. Generally, speaker identity is mostly reflected by the relationship between adjacent tokens, whose extraction mainly depends on local modeling ability. However, the self-attention module, as the key component of transformer, can help the model make full use of global information but insufficient to capture the local information. To tackle this limitation, in this paper, we strengthen the local information modeling from two different aspects: restricting the attention context to be local and introducing convolution operation into transformer. Experiments conducted on Voxceleb illustrate that our proposed methods can notably improve system performance, verifying the significance of local information for speaker verification task.

As the most widely used technique, deep speaker embedding learning has become predominant in speaker verification task recently. Very large neural networks such as ECAPA-TDNN and ResNet can achieve the state-of-the-art performance. However, large models are computationally unfriendly in general, which require massive storage and computation resources. Model compression has been a hot research topic. Parameter quantization usually results in significant performance degradation. Knowledge distillation demands a pretrained complex teacher model. In this paper, we introduce a novel self-knowledge distillation method, namely Self-Knowledge Distillation via Feature Enhancement (SKDFE). It utilizes an auxiliary self-teacher network to distill its own refined knowledge without the need of a pretrained teacher network. Additionally, we apply the self-knowledge distillation at two different levels: label level and feature level. Experiments on Voxceleb dataset show that our proposed self-knowledge distillation method can make small models have comparable or even better performance than large ones. Large models can also be further improved when applying our method.

For self-supervised speaker verification, the quality of pseudo labels decides the upper bound of the system due to the massive unreliable labels.In this work, we propose dynamic loss-gate and label correction (DLG-LC) to alleviate the performance degradation caused by unreliable estimated labels.In DLG, we adopt Gaussian Mixture Model (GMM) to dynamically model the loss distribution and use the estimated GMM to distinguish the reliable and unreliable labels automatically.Besides, to better utilize the unreliable data instead of dropping them directly, we correct the unreliable label with model predictions.Moreover, we apply the negative-pairs-free DINO framework in our experiments for further improvement.Compared to the best-known speaker verification system with self-supervised learning, our proposed DLG-LC converges faster and achieves 11.45%, 18.35% and 15.16% relative improvement on Vox-O, Vox-E and Vox-H trials of Voxceleb1 evaluation dataset.

In real application scenarios, it is often challenging to obtain a large amount of labeled data for speaker representation learning due to speaker privacy concerns. Self-supervised learning with no labels has become a more and more promising way to solve it. Compared with contrastive learning, self-distilled approaches use only positive samples in the loss function and thus are more attractive. In this paper, we present a comprehensive study on self-distilled self-supervised speaker representation learning, especially on critical data augmentation. Our proposed strategy of audio perturbation augmentation has pushed the performance of the speaker representation to a new limit. The experimental results show that our model can achieve a new SoTA on Voxceleb 1 speaker verification evaluation benchmark (i.e., equal error rate (EER) 2.505%, 2.473%, and 4.791 % for trial Vox1-O, Vox1-E and Vox1-H, respectively), discarding any speaker labels in the training phase.

Large performance degradation is often observed for speaker verification systems when applied to a new domain dataset. Given an unlabeled target-domain dataset, unsupervised domain adaptation (UDA) methods, which usually leverage adversarial training strategies, are commonly used to bridge the performance gap caused by the domain mismatch. However, such adversarial training strategy only uses the distribution information of target domain data and can not ensure the performance improvement on the target domain. In this paper, we incorporate self-supervised learning strategy to the unsupervised domain adaptation system and proposed a self-supervised learning based domain adaptation approach (SSDA). Compared to the traditional UDA method, the new SSDA training strategy can fully leverage the potential label information from target domain and adapt the speaker discrimination ability from source domain simultaneously. We evaluated the proposed approach on the Vox-Celeb (labeled source domain) and CnCeleb (unlabeled target domain) datasets, and the best SSDA system obtains 10.2% Equal Error Rate (EER) on the CnCeleb dataset without using any speaker labels on CnCeleb, which also can achieve the state-of-the-art results on this corpus.

This paper presents the SJTU system for both text-dependent and text-independent tasks in short-duration speaker verification (SdSV) challenge 2021.In this challenge, we explored different strong embedding extractors to extract robust speaker embedding.For text-independent task, language-dependent adaptive snorm is explored to improve the system performance under the cross-lingual verification condition.For text-dependent task, we mainly focus on the in-domain fine-tuning strategies based on the model pre-trained on large-scale out-of-domain data.In order to improve the distinction between different speakers uttering the same phrase, we proposed several novel phrase-aware fine-tuning strategies and phrase-aware neural PLDA.With such strategies, the system performance is further improved.Finally, we fused the scores of different systems, and our fusion systems achieved 0.0473 in Task1 (rank 3) and 0.0581 in Task2 (rank 8) on the primary evaluation metric.

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.

Using deep neural network to extract speaker embedding has significantly improved the speaker verification task. However, such embeddings are still vulnerable to channel variability. Previous works have used adversarial training to suppress channel information to extract channel-invariant embedding and achieved a significant improvement. Inspired by the successful joint multi-task and adversarial training with phonetic information for phonetic-invariant speaker embedding learning, in this paper, a similar methodology is developed to suppress the channel variability. By treating the recording devices or environments as the channel variability, two individual experiments are carried out, and consistent performance improvement is observed in both cases. The best performance is obtained by sequentially applying multi-task training at the statistics pooling layer and adversarial training at the embedding layer, achieving 10.77% and 9.37% relative improvements in terms of EER compared to the baselines, for the recording environments or devices level, respectively.