Proof pending. Core topic summary fields are still materializing.
Recent advancements in speech AI are focusing on enhancing multilingual capabilities, improving robustness against contextual biases, and refining speech synthesis quality. New benchmarks for languages like Korean and Arabic are being developed to evaluate speech language models more effectively. Techniques to mitigate hallucinations in speech models and frameworks for detecting speaker drift are also emerging. Additionally, unified models that integrate speech generation and understanding are being explored, alongside tools for identifying spurious correlations in speech datasets. These developments are crucial for builders aiming to create more reliable and versatile speech applications that can cater to diverse linguistic and contextual needs.
Topic-specific paper and score movement from the daily diff ledger.
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-...
Speech language models (SpeechLMs) have achieved substantial progress by extending large language models (LLMs) to the speech modality. However, SpeechLM evaluation remains heavily centered on English...
Contextual automatic speech recognition (ASR) with Speech-LLMs is typically trained with oracle conversation history, but relies on error-prone history at inference, causing a train-test mismatch in t...
We present Ara-BEST-RQ, a family of self-supervised learning (SSL) models specifically designed for multi-dialectal Arabic speech processing. Leveraging 5,640 hours of crawled Creative Commons speech ...
Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift, a subtle, gradual shift in perceived speaker identity within a sing...
Unified speech foundation models require a holistic tokenization space that is both learnable by language models and decodable into high-quality waveforms. Existing speech tokenizers, however, often f...
Hallucinations in Speech Large Language Models (SpeechLLMs) pose significant risks, yet existing detection methods typically rely on gold-standard outputs that are costly or impractical to obtain. Mor...
Adapting pre-trained text Large Language Models (LLMs) into Speech Language Models (Speech LMs) via continual pretraining on speech data is promising, but often degrades the original text capabilities...
We introduce a toolkit for uncovering spurious correlations between recording characteristics and target class in speech datasets. Spurious correlations may arise due to heterogeneous recording condit...
In speech evaluation, an Automatic Speech Recognition (ASR) model often computes time boundaries and phoneme posteriors for input features. However, limited data for ASR training hinders expansion of ...
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Canonical route: /topics
Agent Handoff
Canonical ID speech-ai | Route /topic/speech-ai
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/speech-aiMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Speech AI",
"cluster": "Speech AI"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Speech AI",
"normalized_query": "speech-ai",
"route": "/topic/speech-ai",
"paper_ref": null,
"topic_slug": "speech-ai",
"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.