# Speaker Characterization by means of Attention Pooling

arXiv: https://arxiv.org/abs/2405.04096 · HTML: https://mlsanity.com/paper/2405.04096/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 7 May 2024.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 14 of 14)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System](https://mlsanity.com/paper/1804.05160/) (2018)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM](https://mlsanity.com/paper/1706.02737/) (2017)
- [Large Margin Softmax Loss for Speaker Verification](https://mlsanity.com/paper/1904.03479/) (2019)
- [x-vectors meet emotions: A study on dependencies between emotion and speaker recognition](https://mlsanity.com/paper/2002.05039/) (2020)
- [The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates](https://mlsanity.com/paper/2102.13468/) (2021)
- [Self Multi-Head Attention for Speaker Recognition](https://mlsanity.com/paper/1906.09890/) (2019)
- [Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks](https://mlsanity.com/paper/2012.14553/) (2020)
- [Language Identification Using Deep Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1708.04811/) (2017)
- [Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning](https://mlsanity.com/paper/1708.03920/) (2017)
- [Double Multi-Head Attention for Speaker Verification](https://mlsanity.com/paper/2007.13199/) (2020)
- [Spatial Pyramid Encoding with Convex Length Normalization for Text-Independent Speaker Verification](https://mlsanity.com/paper/1906.08333/) (2019)

## Abstract (arXiv)

State-of-the-art Deep Learning systems for speaker verification are commonly based on speaker embedding extractors. These architectures are usually composed of a feature extractor front-end together with a pooling layer to encode variable-length utterances into fixed-length speaker vectors. The authors have recently proposed the use of a Double Multi-Head Self-Attention pooling for speaker recognition, placed between a CNN-based front-end and a set of fully connected layers. This has shown to be an excellent approach to efficiently select the most relevant features captured by the front-end from the speech signal. In this paper we show excellent experimental results by adapting this architecture to other different speaker characterization tasks, such as emotion recognition, sex classification and COVID-19 detection.