Conditional Conformer: Improving Speaker Modulation for Single and Multi-User Speech Enhancement

Recently, Feature-wise Linear Modulation (FiLM) has been shown to outperform other approaches to incorporate speaker embedding into speech separation and VoiceFilter models. We propose an improved method of incorporating such embeddings into a VoiceFilter frontend for automatic speech recognition (ASR) and textindependent speaker verification (TI-SV). We extend the widelyused Conformer architecture to construct a FiLM Block with additional feature processing before and after the FiLM layers. Apart from its application to single-user VoiceFilter, we show that our system can be easily extended to multi-user VoiceFilter models via element-wise max pooling of the speaker embeddings in a projected space. The final architecture, which we call Conditional Conformer, tightly integrates the speaker embeddings into a Conformer backbone. We improve TI-SV equal error rates by as much as 56% over prior multi-user VoiceFilter models, and our element-wise max pooling reduces relative WER compared to an attention mechanism by as much as 10%