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The DOGE Boys Get VC Funding to Support Their Latest Enterprise
Former members of the so-called Department of Government Efficiency (DOGE) have launched a startup to, they say, bring “DOGE for the private sector.” Their holding company, called Special, has backing from billionaire Marc Andreessen’s venture capital firm a16z and several other former DOGE members. In a post on a16z’s Substack, Nate Cavanaugh and Justin Fox, who led DOGE’s efforts at several government agencies, write that their startup will build “an operating system to transform critical...
Three of our worst VC stories
https://xcancel.com/eastdakota/status/2062860530360959273 Comments URL: https://news.ycombinator.com/item?id=48416845 Points: 4 # Comments: 0
Vitality capacity preservation through lifelong aerobic exercise: a pathway to healthy ageing
Background The distinction between healthy and pathological ageing has led to the concept of vitality capacity (VC), which can be understood as the body physiological reserve. An individual VC can be estimated using 12 biomarkers spread across 3 domains: immune and stress response, energy and metabolism and neuromuscular function. Vitality capacity may be preserved by lifelong physical activity.
MeanVC 2: Robust Low-Latency Streaming Zero-Shot Voice Conversion
arXiv:2606.09050v1 Announce Type: cross Abstract: Streaming zero-shot voice conversion (VC) has become increasingly popular due to its potential for real-time applications. The recently proposed MeanVC achieves lightweight streaming zero-shot VC, but it has several limitations: its chunk-wise autoregressive denoising doubles the effective training sequence length, conversion quality degrades under small-chunk settings, and its timbre encoder directly relies on reference mel-spectrograms,...
Advancing Electrolaryngeal Speech Enhancement Through Speech-Text Representation Learning
arXiv:2606.01905v1 Announce Type: cross Abstract: Objective: laryngectomees depend on an electromechanical device to generate electrolaryngeal (EL) speech. Compared with normal speech, EL speech suffers from severe distortion, limited phonetic variation, unnatural prosody, and temporal shifts, degrading naturalness and intelligibility. Although sequence-to-sequence (seq2seq) voice conversion (VC) based EL-speech-to-normal-speech conversion (EL2SP) is promising, substantial mismatches between...
From A to B to A: Palindromic Zero-Shot Voice Conversion with Non-Parallel Data
Announce Type: new Abstract: We present a voice conversion (VC) framework that utilizes K-Nearest Neighbors (KNN) retrieval over WavLM representations to align non-parallel source and target speech, constructing synthetic training pairs for supervised learning. The retrieved segments serve as synthetic inputs, while real target audio provides ground-truth outputs, forming a synthetic-to-real training paradigm that naturally supports multilingual data without requiring parallel corpora or...
Universal Speech Content Factorization
arXiv:2603.08977v2 Announce Type: replace-cross Abstract: We propose Universal Speech Content Factorization (USCF), a simple and invertible linear method for extracting a low-rank speech representation in which speaker timbre is suppressed while phonetic content is preserved. USCF extends Speech Content Factorization, a closed-set voice conversion (VC) method, to an open-set setting by learning a universal speech-to-content mapping via least-squares optimization and deriving speaker-specific...
Tight Sample Complexity of Transformers
arXiv:2606.09731v1 Announce Type: new Abstract: We tightly characterize the VC dimension of depth-$L$ Transformers with a total of $W$ parameters, mapping an input sequence of length $T$ to a single output, establishing an upper bound of $O(L W \log (T W))$ and a nearly matching lower bound of $\Omega(L W \log (T W / L))$. We further tightly characterize the sample complexity of chain-of-thought learning using such a Transformer, showing teacher forcing (i.e. selecting a predictor consistent...