← Back to all news
arXiv Machine Learning By Quentin Le Tellier, Albert Rilliard, Olivier Perrotin, Marc Evrard

Vocal Intensity Conversion with Continuous SPL Control via Adversarial Training

Read the original on arXiv Machine Learning →

The paper presents a lightweight vocal intensity conversion system that uses a conditional cycle‑consistent GAN to transform speech features so that the output matches any target sound pressure level (SPL) on a continuous decibel scale, rather than relying on discrete categories or reference utterances. The converter operates on features from a frozen speech encoder and is paired with a pretrained vocoder for resynthesis. Experiments comparing log‑mel spectrograms, WavLM, and SpeechTokenizer representations show that the WavLM‑based model achieves 83% of the target intensity change and also shifts the fundamental frequency, with perceptual tests confirming that perceived intensity rises with the target level on amplitude‑normalized stimuli.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Oct 1

MeanVoiceFlow2: Joint Optimization of Mean Flow and Content Encoder for Fast One-Step Zero-Shot Voice Conversion

MeanVoiceFlow2 is a new voice conversion framework that jointly optimizes a flow-based conversion module and a computationally efficient content encoder. It is trained via conversion distillation from MeanVoiceFlow and real data reconstruction, and further enhanced with diffusion-GAN training, sample mixing, and teacher-guided conditioning augmentation. Experiments on zero-shot voice conversion show that MeanVoiceFlow2 delivers higher perceptual quality and about nine times faster inference than its predecessor while preserving speaker similarity.

By Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka, Yuto Kondo
diffusionefficiency
More like this →
arXiv AI
Oct 16

Bridging the Gap: Converting Read Text to Conversational Dialogue

The paper introduces Prosodic Adjustment with Conversational Context (PACC), a deep‑learning method that converts read speech into natural conversational speech. PACC analyzes and modifies prosodic features—intonation, stress, and rhythm—using HiFi‑GAN for synthesis, aiming to preserve naturalness and intelligibility while keeping computational load low for real‑time use. Experiments show significant improvements in speech conversion, higher model accuracy, and better Mean Opinion Score (MOS) results, establishing new benchmarks for the task.

By Parshav Singla, Agnik Banerjee, Aaditya Arora, Shruti Aggarwal, Anil Kumar Verma, Vikram C M, Raj Prakash Gohil, Gopal Kumar Agarwal
diffusionbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 29

A Comprehensive Study of Content Representations for Speech Synthesis

The paper investigates how different speech content representations—such as SSL features, supervised tokens, posteriorgrams, and neural audio codecs—perform when used to train a generative model that produces audio conditioned only on each representation. By evaluating the generated audio on content, speaker identity, and prosody, the study identifies two regimes: some representations almost fully reconstruct the original audio, while others effectively separate speaker identity. The findings reveal that disentanglement of speaker identity depends on both the training objective and the representation’s information capacity, rather than supervision alone.

By Diego Torres, Axel Roebel, Nicolas Obin
llmsmultimodal
More like this →