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Notes on speech recognition

11 notes

  1. Finetuning Strategies for Querying Sounds by Vocal Imitation

    Source note · Aug 19, 2026 · ai, machine learning, speech recognition

    The authors describe their winning strategy for a sound effect querying competition in 2025, which involved two fine-tuning strategies: contrastive learning with a frozen pretrained CED encoder and joint…

  2. Whisper-Aware LLM: Self-Supervised Uncertainty Learning for Robust Whispered Speech Recognition

    Source note · Aug 11, 2026 · machine learning, speech recognition, self-supervised learning

    The Whisper-Aware LLM is a new framework designed to improve the performance of ASR (Automatic Speech Recognition) systems for whispered speech. The model learns to perceive and react to the uncertainty associated with…

  3. Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors

    Source note · Aug 6, 2026 · ai, nlp, speech recognition

    Researchers used Concept Activation Vectors (CAVs) to analyze the bias of two automatic L2 speaking assessment systems: a text-based BERT grader and a multimodal grader based on Whisper. The study shows that the…

  4. Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors

    Source note · Aug 6, 2026 · speech recognition

    Researchers analyze the fairness and interpretability of two automatic speaking assessment systems using Concept Activation Vectors (CAVs). They find that the recoverability and sensitivity to certain concepts depend…

  5. Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing

    Source note · Jul 21, 2026 · machine learning, natural language processing, speech recognition

    The authors propose a method to improve the accuracy of automatic speech recognition (ASR) models by controlling the activation of 'verbatim' versus 'intended' transcription styles. They argue that current models…

  6. Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data

    Source note · Jul 20, 2026 · ai, autonomous agents, speech recognition

    Researchers tested two coding agents, Claude Code and OpenAI Codex, on a real-world task of identifying Quranic verses in noisy speech transcripts. Both agents developed similar algorithms initially but diverged later…

  7. Audio-Native Speech Recognition with a Frozen Discrete-Diffusion Language Model

    Source note · Jul 14, 2026 · artificial intelligence, machine learning, natural language processing

    Researchers train a discrete diffusion language model to transcribe speech instead of using autoregressive decoders that emit one token at a time. The model is based on a mixture-of-experts called DiffusionGemma, and…

  8. When Synthetic Speech Is All You Have: Better Call GRPO

    Source note · Jul 9, 2026 · ai, reinforcement learning, speech recognition

    Researchers present a method called Group Relative Policy Optimization (GRPO) that improves the accuracy of synthetic speech-to-text conversion compared to traditional methods. This could be particularly useful in…

  9. Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas

    Source note · Jul 2, 2026 · artificial intelligence, machine learning, speech recognition

    Researchers present two contributions aimed at improving speaker recognition in long-form TV dramas. They create a large-scale benchmark called DramaSR-532K with over 900 characters and propose using a large reasoning…

  10. Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas

    Source note · Jul 2, 2026 · artificial intelligence, speech recognition

    The researchers introduce a large-scale benchmark called DramaSR-532K for speaker recognition in long-form TV dramas and propose a new method, DramaSR-LRM, built on a large reasoning model (LRM). This method aggregates…

  11. TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech

    Source note · Jun 29, 2026 · artificial intelligence, natural language processing, speech recognition

    Researchers introduce a dataset (DyadEE) for studying emotional entrainment in dyadic speech interactions, including both entrained and disrupted conversations. They also propose TRACE, a new framework that models…

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