# LATTE

Target speaker extraction for multilingual overlapping speech no longer applies one fixed computation depth to every input. Fixed-depth frontends either under-process hard inputs or waste computation on inputs that have already converged. LATTE uses a difficulty estimator to route each mixture through two, four, or six shared extractor blocks, reaching 15.07 dB macro SI-SNRi at 0.30 GFLOPs on the eight-language PolyExtract benchmark, within 0.09 dB of a 0.53-GFLOP fixed-depth path while cutting computation by 43.4%; before frozen Qwen3-Omni it lowers average WER from TF-GridNet's 16.8% to 14.6%.

Target speaker extraction for multilingual overlapping speech no longer applies one fixed computation depth to every input. Fixed-depth frontends either under-process hard inputs or waste computation on inputs that have already converged. LATTE uses a difficulty estimator to route each mixture through two, four, or six shared extractor blocks, reaching 15.07 dB macro SI-SNRi at 0.30 GFLOPs on the eight-language PolyExtract benchmark, within 0.09 dB of a 0.53-GFLOP fixed-depth path while cutting computation by 43.4%; before frozen Qwen3-Omni it lowers average WER from TF-GridNet's 16.8% to 14.6%.