mlsanityData to 19 Oct 2026

Phonetic Richness for Improved Automatic Speaker Verification

arXiv 2407.08017 ↗First posted 10 Jul 2024eess.AS cs.SDcited by 0

On the map: Speech and audio language models → Speaker recognition and diarization

Abstract (arXiv)

When it comes to authentication in speaker verification systems, not all utterances are created equal. It is essential to estimate the quality of test utterances in order to account for varying acoustic conditions. In addition to the net-speech duration of an utterance, it is observed in this paper that phonetic richness is also a key indicator of utterance quality, playing a significant role in accurate speaker verification. Several phonetic histogram based formulations of phonetic richness are explored using transcripts obtained from an automatic speaker recognition system. The proposed phonetic richness measure is found to be positively correlated with voice authentication scores across evaluation benchmarks. Additionally, the proposed measure in combination with net speech helps in calibrating the speaker verification scores, obtaining a relative EER improvement of 5.8% on the Voxceleb1 evaluation protocol. The proposed phonetic richness based calibration provides higher benefit for short utterances with repeated words.

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 9 of its 28 references that are in the map. First posted on arXiv 10 Jul 2024.

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  1. 2022Investigation of Different Calibration Methods for Deep Speaker Embedding based Verification Systems28 Mar

Other references · 8

  1. 2018VoxCeleb2: Deep Speaker Recognitioncited by 1,166
  2. 2017VoxCeleb: a large-scale speaker identification datasetcited by 985
  3. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 980
  4. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 532
  5. 2019QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutionscited by 118
  6. 2020The IDLAB VoxSRC-20 Submission: Large Margin Fine-Tuning and Quality-Aware Score Calibration in DNN Based Speaker Verificationcited by 69
  7. 2019Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plancited by 14
  8. 2020Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairscited by 14

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  1. 2026PhiNet: Speaker Verification with Phonetic Interpretability
  2. 2025ExPO: Explainable Phonetic Trait-Oriented Network for Speaker Verification
  3. 2024PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification
  4. 2026CoLMbo-SV: A Grounded Language Model for Explainable Speaker Verification
  5. 2024Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization
  6. 2025Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker Traits and Speech Properties
  7. 2026Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification
  8. 2026Multimodal Speaker Verification as a Threat to Speaker Anonymization