# A Teacher-Student approach for extracting informative speaker embeddings from speech mixtures

arXiv: https://arxiv.org/abs/2306.00634 · HTML: https://mlsanity.com/paper/2306.00634/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Speech and audio distillation, one of 12 directions in Knowledge distillation.
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Placed in

- Knowledge distillation / [Speech and audio distillation](https://mlsanity.com/distill/speech-and-audio-distillation/)

## Builds on (most cited first, 23 of 23)

- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks](https://mlsanity.com/paper/1703.06284/) (2017)
- [End-to-end speaker segmentation for overlap-aware resegmentation](https://mlsanity.com/paper/2104.04045/) (2021)
- [Wavesplit: End-to-End Speech Separation by Speaker Clustering](https://mlsanity.com/paper/2002.08933/) (2020)
- [Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks](https://mlsanity.com/paper/2012.14952/) (2020)
- [Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario](https://mlsanity.com/paper/2005.07272/) (2020)
- [Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis](https://mlsanity.com/paper/2011.02014/) (2020)
- [Large Margin Softmax Loss for Speaker Verification](https://mlsanity.com/paper/1904.03479/) (2019)
- [Deep Neural Networks for Multiple Speaker Detection and Localization](https://mlsanity.com/paper/1711.11565/) (2017)
- [Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2010.11458/) (2020)
- [Simple Attention Module based Speaker Verification with Iterative noisy label detection](https://mlsanity.com/paper/2110.06534/) (2021)
- [VoxSRC 2021: The Third VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2201.04583/) (2022)
- [Multi-class Spectral Clustering with Overlaps for Speaker Diarization](https://mlsanity.com/paper/2011.02900/) (2020)
- [The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2109.02002/) (2021)
- [Tackling the Score Shift in Cross-Lingual Speaker Verification by Exploiting Language Information](https://mlsanity.com/paper/2110.09150/) (2021)
- [SJTU-AISPEECH System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.09076/) (2022)
- [MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator](https://mlsanity.com/paper/2209.11494/) (2022)
- [Speaker activity driven neural speech extraction](https://mlsanity.com/paper/2101.05516/) (2021)
- [Speaker Verification in Multi-Speaker Environments Using Temporal Feature Fusion](https://mlsanity.com/paper/2206.13808/) (2022)

## Built on by (most cited first, 1 of 1)

- [Towards Low-Latency Tracking of Multiple Speakers With Short-Context Speaker Embeddings](https://mlsanity.com/paper/2508.14115/) (2025)

## Abstract (arXiv)

We introduce a monaural neural speaker embeddings extractor that computes an embedding for each speaker present in a speech mixture. To allow for supervised training, a teacher-student approach is employed: the teacher computes the target embeddings from each speaker's utterance before the utterances are added to form the mixture, and the student embedding extractor is then tasked to reproduce those embeddings from the speech mixture at its input. The system much more reliably verifies the presence or absence of a given speaker in a mixture than a conventional speaker embedding extractor, and even exhibits comparable performance to a multi-channel approach that exploits spatial information for embedding extraction. Further, it is shown that a speaker embedding computed from a mixture can be used to check for the presence of that speaker in another mixture.