# DDTSE: Discriminative Diffusion Model for Target Speech Extraction

arXiv: https://arxiv.org/abs/2309.13874 · HTML: https://mlsanity.com/paper/2309.13874/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 29 of 29)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020)
- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors](https://mlsanity.com/paper/2110.01763/) (2021)
- [WHAM!: Extending Speech Separation to Noisy Environments](https://mlsanity.com/paper/1907.01160/) (2019)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech](https://mlsanity.com/paper/2207.06389/) (2022)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2212.11851/) (2022)
- [Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain](https://mlsanity.com/paper/2203.17004/) (2022)
- [Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit](https://mlsanity.com/paper/2210.17016/) (2022)
- [VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking](https://mlsanity.com/paper/1810.04826/) (2018)
- [Diffusion-based Generative Speech Source Separation](https://mlsanity.com/paper/2210.17327/) (2022)
- [Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration](https://mlsanity.com/paper/2211.02397/) (2022)
- [Neural Target Speech Extraction: An Overview](https://mlsanity.com/paper/2301.13341/) (2023)
- [Cold Diffusion for Speech Enhancement](https://mlsanity.com/paper/2211.02527/) (2022)
- [SpEx: Multi-Scale Time Domain Speaker Extraction Network](https://mlsanity.com/paper/2004.08326/) (2020)
- [Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement](https://mlsanity.com/paper/2302.14748/) (2023)
- [Improving GANs for Speech Enhancement](https://mlsanity.com/paper/2001.05532/) (2020)
- [Target Speech Extraction with Conditional Diffusion Model](https://mlsanity.com/paper/2308.03987/) (2023)
- [Single and Few-step Diffusion for Generative Speech Enhancement](https://mlsanity.com/paper/2309.09677/) (2023)
- [Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement](https://mlsanity.com/paper/2210.17287/) (2022)
- [Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders](https://mlsanity.com/paper/2305.10734/) (2023)
- [Diffusion-based speech enhancement with a weighted generative-supervised learning loss](https://mlsanity.com/paper/2309.10457/) (2023)
- [DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction](https://mlsanity.com/paper/2112.13520/) (2021)

## Built on by (most cited first, 8 of 8)

- [CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations](https://mlsanity.com/paper/2404.06690/) (2024)
- [SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline](https://mlsanity.com/paper/2505.19314/) (2025)
- [FlowTSE: Target Speaker Extraction with Flow Matching](https://mlsanity.com/paper/2505.14465/) (2025)
- [MeanFlow-TSE: One-Step Generative Target Speaker Extraction with Mean Flow](https://mlsanity.com/paper/2512.18572/) (2025)
- [Adaptive Deterministic Flow Matching for Target Speaker Extraction](https://mlsanity.com/paper/2510.16995/) (2025)
- [Audio Generation Through Score-Based Generative Modeling: Design Principles and Implementation](https://mlsanity.com/paper/2506.08457/) (2025)
- [AlphaFlowTSE: One-Step Generative Target Speaker Extraction via Conditional AlphaFlow](https://mlsanity.com/paper/2603.10701/) (2026)
- [Mask2Flow-TSE: Two-Stage Target Speaker Extraction with Masking and Flow Matching](https://mlsanity.com/paper/2603.12837/) (2026)

## Abstract (arXiv)

Diffusion models have gained attention in speech enhancement tasks, providing an alternative to conventional discriminative methods. However, research on target speech extraction under multi-speaker noisy conditions remains relatively unexplored. Moreover, the superior quality of diffusion methods typically comes at the cost of slower inference speed. In this paper, we introduce the Discriminative Diffusion model for Target Speech Extraction (DDTSE). We apply the same forward process as diffusion models and utilize the reconstruction loss similar to discriminative methods. Furthermore, we devise a two-stage training strategy to emulate the inference process during model training. DDTSE not only works as a standalone system, but also can further improve the performance of discriminative models without additional retraining. Experimental results demonstrate that DDTSE not only achieves higher perceptual quality but also accelerates the inference process by 3 times compared to the conventional diffusion model.