# TristouNet: Triplet Loss for Speaker Turn Embedding

arXiv: https://arxiv.org/abs/1609.04301 · HTML: https://mlsanity.com/paper/1609.04301/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2016.

## Built on by (most cited first, 18 of 18)

- [Time Series Contrastive Learning with Information-Aware Augmentations](https://mlsanity.com/paper/2303.11911/) (2023)
- [End-to-End Speech Recognition from Federated Acoustic Models](https://mlsanity.com/paper/2104.14297/) (2021)
- [Learning Efficient Representations for Keyword Spotting with Triplet Loss](https://mlsanity.com/paper/2101.04792/) (2021)
- [Token-Level Supervised Contrastive Learning for Punctuation Restoration](https://mlsanity.com/paper/2107.09099/) (2021)
- [Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition](https://mlsanity.com/paper/2104.01989/) (2021)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [The effect of speech pathology on automatic speaker verification -- a large-scale study](https://mlsanity.com/paper/2204.06450/) (2022)
- [Driver2vec: Driver Identification from Automotive Data](https://mlsanity.com/paper/2102.05234/) (2021)
- [Real Additive Margin Softmax for Speaker Verification](https://mlsanity.com/paper/2110.09116/) (2021)
- [Speaker attribution with voice profiles by graph-based semi-supervised learning](https://mlsanity.com/paper/2102.03634/) (2021)
- [GE2E-KWS: Generalized End-to-End Training and Evaluation for Zero-shot Keyword Spotting](https://mlsanity.com/paper/2410.16647/) (2024)
- [Adaptive Margin Circle Loss for Speaker Verification](https://mlsanity.com/paper/2106.08004/) (2021)
- [Supervised Training of Siamese Spiking Neural Networks with Earth Mover's Distance](https://mlsanity.com/paper/2203.13207/) (2022)
- [Triplet loss based embeddings for forensic speaker identification in Spanish](https://mlsanity.com/paper/2102.12564/) (2021)
- [Deep Metric Learning Model for Imbalanced Fault Diagnosis](https://mlsanity.com/paper/2107.03786/) (2021)
- [Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data](https://mlsanity.com/paper/2112.14314/) (2021)
- [Multi-microphone Automatic Speech Segmentation in Meetings Based on Circular Harmonics Features](https://mlsanity.com/paper/2306.04268/) (2023)
- [Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity](https://mlsanity.com/paper/2409.16416/) (2024)

## Abstract (arXiv)

TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space. Thanks to the triplet loss paradigm used for training, the resulting sequence embeddings can be compared directly with the euclidean distance, for speaker comparison purposes. Experiments on short (between 500ms and 5s) speech turn comparison and speaker change detection show that TristouNet brings significant improvements over the current state-of-the-art techniques for both tasks.