# Rhythm Features for Speaker Identification

arXiv: https://arxiv.org/abs/2506.06834 · HTML: https://mlsanity.com/paper/2506.06834/

## Key facts

- Cited by 1 papers in the map (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 23 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Placed in

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

## Builds on (most cited first, 9 of 9)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Probing the Information Encoded in X-vectors](https://mlsanity.com/paper/1909.06351/) (2019)
- [Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization](https://mlsanity.com/paper/2412.17164/) (2024)
- [An empirical analysis of information encoded in disentangled neural speaker representations](https://mlsanity.com/paper/2002.03520/) (2020)
- [Improving Automatic Emotion Recognition from speech using Rhythm and Temporal feature](https://mlsanity.com/paper/1303.1761/) (2013)

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

- [Multimodal Speaker Verification as a Threat to Speaker Anonymization](https://mlsanity.com/paper/2607.19636/) (2026)

## Abstract (arXiv)

While deep learning models have demonstrated robust performance in speaker recognition tasks, they primarily rely on low-level audio features learned empirically from spectrograms or raw waveforms. However, prior work has indicated that idiosyncratic speaking styles heavily influence the temporal structure of linguistic units in speech signals (rhythm). This makes rhythm a strong yet largely overlooked candidate for a speech identity feature. In this paper, we test this hypothesis by applying deep learning methods to perform text-independent speaker identification from rhythm features. Our findings support the usefulness of rhythmic information for speaker recognition tasks but also suggest that high intra-subject variability in ad-hoc speech can degrade its effectiveness.