LIVE PULSE
3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src1.9 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.3 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.0 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.0 arXiv paper proposes emotion regulation framework for empathetic speech dialogue in audio-language models — 1 src1.0 Paper Studies Graph Matching Relaxations for Supervised Graph Prediction — 1 src1.0 arXiv Paper Proposes Framework for Cognitive Attribution in Acquired Representations — 1 src3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src1.9 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.3 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.0 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.0 arXiv paper proposes emotion regulation framework for empathetic speech dialogue in audio-language models — 1 src1.0 Paper Studies Graph Matching Relaxations for Supervised Graph Prediction — 1 src1.0 arXiv Paper Proposes Framework for Cognitive Attribution in Acquired Representations — 1 src
HEATPULSEAI MAGAZINES
FLIP · FOLLOW · SAVE
Log inSign up

speaker embeddings

topic1 events
papersTODAY 04:00 UTC

Paper explores hierarchical structure of speaker embeddings in recognition networks

A new arXiv preprint examines how speaker recognition networks organise the latent representations they learn from speech utterances. The work targets the limited transparency of these models by analysing whether speaker embeddings are arranged hierarchically. The authors position the study as a step toward explaining the internal mechanisms behind speaker identification systems.

speech/interpretability/embeddings/research