·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AI’s most important protocol is getting a little bit easier to use21m◆X relaunches a rebuilt Android app after year-long effort1h◆OpenAI is scared of open-weight models. Should the US be?1h◆China’s AI models have Trump’s AI world at war with itself3h◆Adobe’s ‘natural look’ camera app embraces generative AI5h◆Introducing Cosmos 3 Edge5h◆Adobe camera app’s new feature will critique your photos using AI5h◆YouTube clarifies policies around AI slop and upsetting videos5h◆China delivers a one-two punch to America’s AI dominance10h◆Safety and alignment in an era of long-horizon models11h◆AI is more likely than humans to form biases when hiring12h◆ADS-C: Antidistillation Sampling for Classification17h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes17h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems17h◆A Formally Grounded ODRL Evaluator: Implementation and Comparison17h◆Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution17h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents17h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections17h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery17h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI17h◆AI’s most important protocol is getting a little bit easier to use21m◆X relaunches a rebuilt Android app after year-long effort1h◆OpenAI is scared of open-weight models. Should the US be?1h◆China’s AI models have Trump’s AI world at war with itself3h◆Adobe’s ‘natural look’ camera app embraces generative AI5h◆Introducing Cosmos 3 Edge5h◆Adobe camera app’s new feature will critique your photos using AI5h◆YouTube clarifies policies around AI slop and upsetting videos5h◆China delivers a one-two punch to America’s AI dominance10h◆Safety and alignment in an era of long-horizon models11h◆AI is more likely than humans to form biases when hiring12h◆ADS-C: Antidistillation Sampling for Classification17h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes17h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems17h◆A Formally Grounded ODRL Evaluator: Implementation and Comparison17h◆Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution17h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents17h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections17h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery17h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI17h◆
News/Bangla-WhisperDiar: Fine-Tuning Whisper and PyAnnote for Bangla Long-Form Speech Recognition and Speaker Diarization
arxiv
PublishedMay 13, 2026 at 4:00 AM

Bangla-WhisperDiar: Fine-Tuning Whisper and PyAnnote for Bangla Long-Form Speech Recognition and Speaker Diarization

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.08214v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) and speaker diarization in Bangla remain challenging due to long form recordings, diverse acoustic conditions, and significant speaker variability. This work addresses these two core tasks in Bangla spoken language

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivADS-C: Antidistillation Sampling for Classification17harxivBeyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes17harxivFrom Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems17harxivA Formally Grounded ODRL Evaluator: Implementation and Comparison17h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews