Serialized Multi-Layer Multi-Head Attention for Neural Speaker Embedding

Understand

This paper proposes a serialized multi-layer multi-head attention for neural speaker embedding in text-independent speaker verification.

• In prior works, frame-level features from one layer are aggregated to form an utterance-level representation.
• Inspired by the Transformer network, our proposed method utilizes the hierarchical architecture of stacked self-attention mechanisms to derive refined features that are more correlated with speakers.
• Serialized attention mechanism contains a stack of self-attention modules to create fixed-dimensional representations of speakers.