arXiv (All)
open access
Robust Wake-Up Word Detection by Two-stage Multi-resolution Ensembles
Voice-based interfaces rely on a wake-up word mechanism to initiate communication with devices. However, achieving a robust, energy-efficient, and fast detection remains a challenge. This paper addresses these real production needs by enhancing data with temporal alignments and using detection based
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Toward Elastic Speech Inference: Training-Free Wake-Word Detection from Pretrained ASR#1156117
- Cross-Night Modulation of Change Detection ERPs to Foreign Speech Sound Features During N2 Sleep.#988716
- Foreground Voice Activity Detection: Learning Speaker Selectivity from Supervision#1014903
- DriftAudio: Marginal Drifting for Distributional Post-Training of One-Step Text-to-Audio Generators#1064142
- UniVoice: Unifying Autoregressive ASR and Flow-Matching based TTS with Large Language Models#927171
Record · ID 1142512
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive