8 accepted papers
In recent years, there has been a surge in the use of a pre-trained speech model as a feature extractor for speaker verification (SV). To reduce model complexity, researchers transfer knowledge from a pre-trained model to a lightweight student model, enabling the latter to reach a performance level…
Recent works suggest that decoupling the information of non-target speakers from that of the target speaker in knowledge distillation (KD) and subsequently emphasizing the former can lead to significant performance improvement. However, a well-trained teacher model typically produces almost zero non…
Contrastive speaker embedding assumes that the contrast between the positive and negative pairs of speech segments is attributed to speaker identity only. However, this assumption is incorrect because speech signals contain not only speaker identity but also linguistic content. In this paper, we pro…
Recent studies have demonstrated the effectiveness of speaker disentanglement in mitigating the interference caused by speaker features in speech-based depression detection. However, the inherent entanglement between depression features and speaker features poses challenges to depression detection.…
Self-supervised learning (SSL) speech models such as wav2vec and HuBERT have demonstrated state-of-the-art performance on automatic speech recognition (ASR) and proved to be extremely useful in low label-resource settings. However, the success of SSL models has yet to transfer to utterance-level tas…
State-of-the-art speaker verification systems take frame-level acoustics features as input and produce fixed-dimensional embeddings as utterance-level representations. Thus, how to aggregate information from frame-level features is vital for achieving high performance. This paper introduces short-ti…
Domain mismatch is a common problem in speaker verification. This paper proposes an information-maximized variational domain adversarial neural network (InfoVDANN) to reduce domain mismatch by incorporating an InfoVAE into domain adversarial training (DAT). DAT aims to produce speaker discriminative…
How to overcome the training and test data mismatch in speaker verification systems has been a focus of research recently. In this paper, we propose a semi-supervised nuisance attribute network (SNAN) to reduce the domain mismatch in i-vectors and x-vectors. SNANs are based on the idea of nuisance a…