# X-TaSNet: Robust and Accurate Time-Domain Speaker Extraction Network

arXiv: https://arxiv.org/abs/2010.12766 · HTML: https://mlsanity.com/paper/2010.12766/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Heterogeneous Target Speech Separation](https://mlsanity.com/paper/2204.03594/) (2022)
- [X-SepFormer: End-to-end Speaker Extraction Network with Explicit Optimization on Speaker Confusion](https://mlsanity.com/paper/2303.05023/) (2023)
- [Self-Supervised Disentangled Representation Learning for Robust Target Speech Extraction](https://mlsanity.com/paper/2312.10305/) (2023)
- [Single microphone speaker extraction using unified time-frequency Siamese-Unet](https://mlsanity.com/paper/2203.02941/) (2022)
- [Sparsely Overlapped Speech Training in the Time Domain: Joint Learning of Target Speech Separation and Personal VAD Benefits](https://mlsanity.com/paper/2106.14371/) (2021)
- [SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speech](https://mlsanity.com/paper/2206.12132/) (2022)
- [Speech Enhancement with Perceptually-motivated Optimization and Dual Transformations](https://mlsanity.com/paper/2209.11905/) (2022)
- [Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation](https://mlsanity.com/paper/2211.02944/) (2022)
- [Spectron: Target Speaker Extraction using Conditional Transformer with Adversarial Refinement](https://mlsanity.com/paper/2409.01352/) (2024)

## Abstract (arXiv)

Extracting the speech of a target speaker from mixed audios, based on a reference speech from the target speaker, is a challenging yet powerful technology in speech processing. Recent studies of speaker-independent speech separation, such as TasNet, have shown promising results by applying deep neural networks over the time-domain waveform. Such separation neural network does not directly generate reliable and accurate output when target speakers are specified, because of the necessary prior on the number of speakers and the lack of robustness when dealing with audios with absent speakers. In this paper, we break these limitations by introducing a new speaker-aware speech masking method, called X-TaSNet. Our proposal adopts new strategies, including a distortion-based loss and corresponding alternating training scheme, to better address the robustness issue. X-TaSNet significantly enhances the extracted speech quality, doubling SDRi and SI-SNRi of the output speech audio over state-of-the-art voice filtering approach. X-TaSNet also improves the reliability of the results by improving the accuracy of speaker identity in the output audio to 95.4%, such that it returns silent audios in most cases when the target speaker is absent. These results demonstrate X-TaSNet moves one solid step towards more practical applications of speaker extraction technology.