Machine Learning

MERL's Speech & Audio team, led by MERL intern Dominik Klement, ranked 1st out of 11 teams in Track 2, "Offline Target Speaker Extraction," of the Real-TSE Challenge. The challenge focuses on target speaker extraction (TSE) from real-world conversational recordings in either English or Chinese, where the goal is to extract the speech of a target speaker in the presence of interfering speakers, background noise, and reverberation.

While modern TSE systems have achieved strong performance on simulated speech mixtures, their performance can degrade considerably on real-world recordings due to the mismatch between simulated training data and actual conversational environments. The Real-TSE Challenge was designed to advance TSE under these realistic conditions, using real far-field conversational recordings for evaluation.

The MERL team won Track 2 by focusing on training data and curriculum learning rather than introducing a new model architecture. Starting from a strong speech separation model, the team progressively trained the system on fully overlapping synthetic speech, simulated conversations, realistic far-field mixtures, and finally real conversational recordings. This approach reduced the token error rate (TER), measured at either the word (English) or character (Chinese) level, from 70% to 37% on the development set and achieved a final TER of 61.3% on the evaluation set, best among the 11 participating teams. The team also topped the leaderboard in terms of the aggregate ranking across the four measures evaluating intelligibility, target speaker presence rate, speaker similarity, and perceptual quality.

The team also investigated the reliability of the challenge metrics and demonstrated that neural network-based speaker similarity and predicted speech-quality scores could be substantially improved without a corresponding improvement in perceptual quality. Because learned metrics can be susceptible to adversarial attacks or optimization that exploits weaknesses in the metric itself, these findings highlight both the importance of realistic training data for real-world TSE and the need for robust evaluation metrics when developing speech extraction systems.

A paper summarizing the team's findings will be presented at the IEEE Spoken Language Technology (SLT) 2026 workshop, to be held in Palermo, Italy from December 13-16, 2026.