Abstract
This Blog presents “Discover Your Inner Voice,” a method for modeling the perceptual difference between how a
speaker hears their own voice (via bone conduction) and how it is heard by others (via air conduction). Using CAM++
speaker embeddings from the Seed-VC diffusion-based voice conversion pipeline, we extract a 192-dimensional style
delta vector ΔV = V_perceived − V_raw, where V_raw encodes the externally recorded voice and V_perceived
encodes the voice after psychoacoustic skull-resonance filtering. Our key finding — ‖ΔV‖ = 1.191 with cosine
similarity 0.9967 — reveals that CAM++ is inherently invariant to spectral filtering, a desirable property for speaker
verification but a limitation for perceptual style extraction. We discuss the implications, propose a filtered reference
override solution, and outline a roadmap toward clinical applications in laryngeal cancer rehabilitation.
1. Introduction
The question is as old as recorded audio: “Why does my voice sound different on recording?” Every speaker
experiences the disconnect between their internal self-perception and the external recording. This phenomenon arises
from a fundamental psychoacoustic difference: we hear our own voice through two simultaneous pathways — air
conduction through the ear canal and bone conduction through the skull.
While this discrepancy is a familiar curiosity, its computational modeling has remained unexplored. Modern voice
conversion systems can clone a speaker’s voice from a few seconds of reference audio, but they operate on the
external voice — the voice as heard by others. The internal voice — the voice as perceived by the speaker — is lost.
This project introduces the Inner Voice framework: a method to capture the transformation between a speaker’s
external and internal voice using speaker embeddings from a state-of-the-art voice conversion pipeline. We pose the
following research questions:
- Can a psychoacoustic skull-resonance filter produce a measurable shift in speaker embedding space?
2.Can this shift be encoded as a style delta vector (ΔV) for use in voice conversion?
3.Does the ΔV carry perceptually meaningful information about the internal voice?
The scope of this work is a proof-of-concept on a single subject, with a full implementation pipeline including a
Gradio web interface, real-time metrics, and a clinical application framework for laryngeal cancer rehabilitation.
Contributions: – A psychoacoustic skull-resonance filter modeling bone conduction effects – The ΔV = V_perceived
− V_raw style delta extraction pipeline – An override_style parameter for the Seed-VC voice conversion system – A
three-tab Gradio interface with real-time visualization – The experimental finding that CAM++ is filter-invariant to
spectral coloration, quantifying this with ‖ΔV‖ = 1.191 – Open-source release of all code, vectors, and documentation
The remainder of this paper is organized as follows. Section 2 reviews bone conduction psychoacoustics. Section 3
describes the Seed-VC architecture. Section 4 covers the CAM++ speaker encoder. Section 5 presents the proposed
ΔV extraction method. Section 6 details the system implementation. Section 7 reports results and analysis. Section 8
discusses a clinical application. Section 9 concludes with future work.
1.1 Related Work
Voice conversion has evolved through several generations. Early systems used Gaussian mixture models (GMMs) to
map source-to-target spectral envelopes [11]. Parallel data was required — the same utterance from both speakers.
The introduction of cycle-consistent adversarial networks (CycleGAN-VC) [12] enabled non-parallel training,
followed by variational autoencoders (VAEs) and autoencoder-based approaches that disentangled content from
speaker identity.The current state-of-the-art uses diffusion models and self-supervised learning. DiffWave [13], SpecDiff [14], and
VoiceBox [15] demonstrated that diffusion models produce high-fidelity speech. Seed-VC [5] achieved the first truly
zero-shot voice conversion competitive with speaker verification systems, reaching a speaker embedding cosine
similarity (SECS) of 0.867.
Concurrent work in speaker embedding analysis includes studies on embedding invariance [16], which showed that
verification embeddings are robust to channel effects. Our work extends this investigation to the specific case of bone-
conduction modeling.
No prior work has attempted to model the bone-conduction vs. air-conduction perceptual difference using speaker
embeddings. The Inner Voice concept is, to our knowledge, the first attempt to computationally capture the internal
voice perception.
