# Speaker Embedding Extraction with Phonetic Information

arXiv: https://arxiv.org/abs/1804.04862 · HTML: https://mlsanity.com/paper/1804.04862/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [Disentangling Voice and Content with Self-Supervision for Speaker Recognition](https://mlsanity.com/paper/2310.01128/) (2023)
- [Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal](https://mlsanity.com/paper/2212.13023/) (2022)
- [Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation](https://mlsanity.com/paper/2309.03019/) (2023)
- [CoLMbo: Speaker Language Model for Descriptive Profiling](https://mlsanity.com/paper/2506.09375/) (2025)
- [Speech Rhythm-Based Speaker Embeddings Extraction from Phonemes and Phoneme Duration for Multi-Speaker Speech Synthesis](https://mlsanity.com/paper/2402.07085/) (2024)
- [PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification](https://mlsanity.com/paper/2409.05799/) (2024)
- [Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification](https://mlsanity.com/paper/2106.13514/) (2021)
- [Phonetic-aware speaker embedding for far-field speaker verification](https://mlsanity.com/paper/2311.15627/) (2023)
- [Speaker Retrieval in the Wild: Challenges, Effectiveness and Robustness](https://mlsanity.com/paper/2504.18950/) (2025)
- [Identifying Speaker Information in Feed-Forward Layers of Self-Supervised Speech Transformers](https://mlsanity.com/paper/2506.21712/) (2025)

## Abstract (arXiv)

Speaker embeddings achieve promising results on many speaker verification tasks. Phonetic information, as an important component of speech, is rarely considered in the extraction of speaker embeddings. In this paper, we introduce phonetic information to the speaker embedding extraction based on the x-vector architecture. Two methods using phonetic vectors and multi-task learning are proposed. On the Fisher dataset, our best system outperforms the original x-vector approach by 20% in EER, and by 15%, 15% in minDCF08 and minDCF10, respectively. Experiments conducted on NIST SRE10 further demonstrate the effectiveness of the proposed methods.