Adaptive S-norm (AS-norm) calibrates automatic speaker verification (ASV) scores by normalizing them utilize the scores of impostors which are similar to the input speaker. However, AS-norm does not involve any learning process, limiting its ability to provide appropriate regularization strength for various evaluation utterances. To address this limitation, we propose a trainable AS-norm (TAS-norm) that leverages learnable impostor embeddings (LIEs), which are used to compose the cohort. These LIEs are initialized to represent each speaker in a training dataset consisting of impostor speakers. Subsequently, LIEs are fine-tuned by simulating an ASV evaluation. We utilize a margin penalty during top-scoring IEs selection in fine-tuning to prevent non-impostor speakers from being selected. In our experiments with ECAPA-TDNN, the proposed TAS-norm observed 4.11% and 10.62% relative improvement in equal error rate and minimum detection cost function, respectively, on VoxCeleb1-O trial compared with standard AS-norm without using proposed LIEs. We further validated the effectiveness of the TAS-norm on additional ASV datasets comprising Persian and Chinese, demonstrating its robustness across different languages.

In this study, we extend self-distillation with no labels (DINO), a successful self-supervised learning framework, by combining it with supervised classification (SC) for semi-supervised speaker verification with limited labeled data. We introduce a transfer learning framework that pre-trains and fine-tunes the encoder using DINO and SC, respectively, and a multitask learning framework that shares the encoder while having separate projection layers for both methods. To achieve lower inter-speaker similarity, we propose a joint learning framework sharing both the encoder and projection layer for DINO and SC. We also propose an auxiliary contrastive loss between embeddings derived from labeled and unlabeled utterances and introduce a two-stage learning strategy to apply margin penalty effectively. Experimental results on the VoxCeleb corpus indicate that the joint learning framework outperforms the other frameworks and is closest to achieving the performance of fully supervised learning.

Transformer-based end-to-end neural speaker diarization (EEND) models utilize the multi-head self-attention (SA) mechanism to enable accurate speaker label prediction in overlapped speech regions. In this study, to enhance the training effectiveness of SA-EEND models, we propose the use of auxiliary losses for the SA heads of the transformer layers. Specifically, we assume that the attention weight matrices of an SA layer are redundant if their patterns are similar to those of the identity matrix. We then explicitly constrain such matrices to exhibit specific speaker activity patterns relevant to voice activity detection or overlapped speech detection tasks. Consequently, we expect the proposed auxiliary losses to guide the transformer layers to exhibit more diverse patterns in the attention weights, thereby reducing the assumed redundancies in the SA heads. The effectiveness of the proposed method is demonstrated using the simulated and CALLHOME datasets for two-speaker diarization tasks, reducing the diarization error rate of the conventional SA-EEND model by 32.58% and 17.11%, respectively.

Data augmentation using additive noise is a framework for robustly training automatic speech recognition models. To utilize noise information efficiently, previous studies used an additional branch to classify noise conditions. This added branch has a limited effect on the ASR because it performs independently of the ASR branch that classifies senones. In this paper, we propose a noise-aware target extension (NATE) that extends the senone target to contain noise awareness by jointly classifying the senone and noise in a single branch. In the inference stage, the output of the model is processed separately by the noise condition and then aggregated to match the senone posterior distribution. In addition, we combine NATE with self-distillation (NATEsd) to reduce the model parameters and avoid discrepancies between the outputs of training and inference. The effectiveness of the NATE method is validated on the two benchmark development and evaluation sets and simulated noisy test sets, resulting in significant improvements over the previous methods.