# Neural Target Speech Extraction: An Overview

arXiv: https://arxiv.org/abs/2301.13341 · HTML: https://mlsanity.com/paper/2301.13341/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2023.

## Built on by (most cited first, 30 of 33)

- [SpeechX: Neural Codec Language Model as a Versatile Speech Transformer](https://mlsanity.com/paper/2308.06873/) (2023)
- [TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling](https://mlsanity.com/paper/2504.07053/) (2025)
- [Target Speech Extraction with Conditional Diffusion Model](https://mlsanity.com/paper/2308.03987/) (2023)
- [Look Once to Hear: Target Speech Hearing with Noisy Examples](https://mlsanity.com/paper/2405.06289/) (2024)
- [SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline](https://mlsanity.com/paper/2505.19314/) (2025)
- [DDTSE: Discriminative Diffusion Model for Target Speech Extraction](https://mlsanity.com/paper/2309.13874/) (2023)
- [Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction](https://mlsanity.com/paper/2310.07284/) (2023)
- [Listen, Chat, and Remix: Text-Guided Soundscape Remixing for Enhanced Auditory Experience](https://mlsanity.com/paper/2402.03710/) (2024)
- [LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models](https://mlsanity.com/paper/2504.07402/) (2025)
- [End-to-End Joint Target and Non-Target Speakers ASR](https://mlsanity.com/paper/2306.02273/) (2023)
- [Knowledge boosting during low-latency inference](https://mlsanity.com/paper/2407.11055/) (2024)
- [Target conversation extraction: Source separation using turn-taking dynamics](https://mlsanity.com/paper/2407.11277/) (2024)
- [TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models](https://mlsanity.com/paper/2505.06660/) (2025)
- [Steering Deep Non-Linear Spatially Selective Filters for Weakly Guided Extraction of Moving Speakers in Dynamic Scenarios](https://mlsanity.com/paper/2505.14517/) (2025)
- [Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition](https://mlsanity.com/paper/2506.14973/) (2025)
- [Contextual Speech Extraction: Leveraging Textual History as an Implicit Cue for Target Speech Extraction](https://mlsanity.com/paper/2503.08798/) (2025)
- [SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation](https://mlsanity.com/paper/2505.03273/) (2025)
- [Self-Steering Deep Non-Linear Spatially Selective Filters for Efficient Extraction of Moving Speakers under Weak Guidance](https://mlsanity.com/paper/2507.02791/) (2025)
- [Neural Speech Extraction with Human Feedback](https://mlsanity.com/paper/2508.03041/) (2025)
- [TF-MLPNet: Tiny Real-Time Neural Speech Separation](https://mlsanity.com/paper/2508.03047/) (2025)
- [GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model](https://mlsanity.com/paper/2512.20978/) (2025)
- [Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers](https://mlsanity.com/paper/2603.23723/) (2026)
- [StarTSE: Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model](https://mlsanity.com/paper/2604.19635/) (2026)
- [PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction](https://mlsanity.com/paper/2607.08111/) (2026)
- [Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction](https://mlsanity.com/paper/2306.15942/) (2023)
- [ML-ASPA: A Contemplation of Machine Learning-based Acoustic Signal Processing Analysis for Sounds, & Strains Emerging Technology](https://mlsanity.com/paper/2402.10005/) (2023)
- [Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining](https://mlsanity.com/paper/2501.03184/) (2025)
- [End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer Functions](https://mlsanity.com/paper/2502.06285/) (2025)
- [SC-SOT: Conditioning the Decoder on Diarized Speaker Information for End-to-End Overlapped Speech Recognition](https://mlsanity.com/paper/2506.12672/) (2025)
- [Towards Low-Latency Tracking of Multiple Speakers With Short-Context Speaker Embeddings](https://mlsanity.com/paper/2508.14115/) (2025)

## Abstract (arXiv)

Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail-party effect. For decades, researchers have focused on approaching the listening ability of humans. One critical issue is handling interfering speakers because the target and non-target speech signals share similar characteristics, complicating their discrimination. Target speech/speaker extraction (TSE) isolates the speech signal of a target speaker from a mixture of several speakers with or without noises and reverberations using clues that identify the speaker in the mixture. Such clues might be a spatial clue indicating the direction of the target speaker, a video of the speaker's lips, or a pre-recorded enrollment utterance from which their voice characteristics can be derived. TSE is an emerging field of research that has received increased attention in recent years because it offers a practical approach to the cocktail-party problem and involves such aspects of signal processing as audio, visual, array processing, and deep learning. This paper focuses on recent neural-based approaches and presents an in-depth overview of TSE. We guide readers through the different major approaches, emphasizing the similarities among frameworks and discussing potential future directions.