Proof pending. Core topic summary fields are still materializing.
Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in both offline and low-latency streaming settings rema...
Automatic speech recognition replaces typing only when correction costs less than manual entry, a threshold determined by error types, not counts: fixing a misrecognized domain term costs far more tha...
Automatic speech recognition (ASR) systems have achieved near-human accuracy on curated benchmarks, yet still fail in real-world voice agents under conditions that current evaluations do not systemati...
Building competitive automatic speech recognition (ASR) models usually requires large-scale au- dio supervision, which makes reproduction and specialization expensive. We study Ark-ASR, a 0.6B- parame...
Conversational automatic speech recognition in Hungarian is constrained by the limited amount of publicly available dialogue-style training data. The BEA-Dialogue corpus addresses this need, but its s...
Standard LLM-based speech recognition systems typically process utterances in isolation, limiting their ability to leverage conversational context. In this work, we study whether multimodal context fr...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID asr | Route /topic/asr
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/asrMCP example
{
"tool": "search_papers",
"arguments": {
"query": "ASR",
"cluster": "ASR"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "ASR",
"normalized_query": "asr",
"route": "/topic/asr",
"paper_ref": null,
"topic_slug": "asr",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.