arXiv AI July 7, 2026 By Thomas Thebaud, Junhyeok Lee, Laureano Moro-Velazquez, Jesus Villalba Lopez, Najim Dehak

ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions

arXiv:2607. 05276v1 Announce Type: cross Abstract: Speaker embeddings, or x-vectors, are widely used to represent speaker identity and speaker-related attributes, but existing embedding extractors are typically descriptive rather than generative: they map an observed speech segment to an x-vector, which is then used for downstream applications.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 9

GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model

arXiv:2512. 20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and high-fidelity speech.

By Haoyang Li, Xuyi Zhuang, Azmat Adnan, Ye Ni, Wei Rao, Shreyas Gopal, Eng Siong Chng, Boon Siew Han, Yuanjin Zheng

llms rag reinforcement-learning safety

arXiv AI

Jul 7

NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization

arXiv:2607. 03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Speaker Anonymization Systems (SAS).

By Meiying Melissa Chen, Anastasia Kuznetsova, Zhenyu Wang, Zhiyao Duan

rag benchmarks safety

arXiv AI

Jun 19

ZeSTA: Zero-Shot TTS Augmentation with Domain-Conditioned Training for Data-Efficient Personalized Speech Synthesis

arXiv:2603. 04219v2 Announce Type: replace-cross Abstract: We investigate the use of zero-shot text-to-speech (ZS-TTS) as a data augmentation source for low-resource personalized speech synthesis.

By Youngwon Choi, Jinwoo Oh, Hwayeon Kim, Hyeonyu Kim

rag fine-tuning multimodal

arXiv AI

Jul 7

TokAN: Accent Normalization Using Self-Supervised Speech Tokens

arXiv:2607. 03928v1 Announce Type: cross Abstract: Accent normalization (AN) seeks to convert non-native (L2) accented speech into standard (L1) speech while preserving speaker identity.

By Qibing Bai, Shuai Wang, Yuhan Du, Bohan Li, Yannan Wang, Haizhou Li

rag nlp reinforcement-learning fine-tuning efficiency

arXiv AI

Jul 10

PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction

arXiv:2607. 08111v1 Announce Type: cross Abstract: Training target speaker extraction (TSE) models for real conversational mixtures remains challenging because large-scale training corpora and clean target speech for supervision are unavailable.

By Wanyi Ning, Wei Zhou, Yingpeng Li, Yinshang Guo, Haitao Qian, Yiming Cheng

fine-tuning multimodal benchmarks

arXiv AI

Jun 16

An Empirical Study on Learning Latent Representations for Emotional Speech Synthesis

arXiv:2606. 14922v1 Announce Type: cross Abstract: For the last couple of years, the field of speech synthesis has improved dramatically thanks to deep learning.

By Vinh Dang Quang, Huy Ngo Quang

rag

Related stories

GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model

NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization

ZeSTA: Zero-Shot TTS Augmentation with Domain-Conditioned Training for Data-Efficient Personalized Speech Synthesis

TokAN: Accent Normalization Using Self-Supervised Speech Tokens

PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction

An Empirical Study on Learning Latent Representations for Emotional Speech Synthesis