🤖🧠 Granite-Speech-3.3-8B: IBM’s Next-Gen Speech-Language Model for Enterprise AI

🗓️ 14 Oct 2025
📚 AI News & Trends

In the fast-growing field of speech and language AI, IBM continues to make strides with its Granite model family , a suite of open enterprise-grade AI models that combine accuracy, safety and efficiency. The latest addition to this ecosystem, Granite-Speech-3.3-8B marks a significant milestone in automatic speech recognition (ASR) and speech translation (AST) technology. Released ...

#SpeechAI #LanguageModel #EnterpriseAI #ASR #SpeechTranslation #GraniteModel

✨How to Evaluate Speech Translation with Source-Aware Neural MT Metrics

📝 Summary:
This study introduces source-aware metrics for speech translation evaluation by generating text proxies from audio, like ASR transcripts or back-translations. A new re-segmentation algorithm resolves alignment issues. These methods improve evaluation accuracy for speech translation systems.

🔹 Publication Date: Published on Nov 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03295
• PDF: https://arxiv.org/pdf/2511.03295

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#SpeechTranslation #NMTMetrics #ASR #NLP #DeepLearning

🤖🧠 IndicWav2Vec: Building the Future of Speech Recognition for Indian Languages

🗓️ 09 Dec 2025
📚 AI News & Trends

India is one of the most linguistically diverse countries in the world, home to over 1,600 languages and dialects. Yet, speech technology for most of these languages has historically lagged behind due to limited data and resources. While English and a handful of global languages have benefited immensely from advancements in automatic speech recognition (ASR), ...

#IndicWav2Vec #SpeechRecognition #IndianLanguages #ASR #LinguisticDiversity #AIResearch

✨End-to-End Joint ASR and Speaker Role Diarization with Child-Adult Interactions

📝 Summary:
This paper presents a unified end-to-end framework extending Whisper for joint ASR and child-adult speaker role diarization. It significantly improves transcription accuracy and scalability by preventing error propagation, achieving lower word error rates and competitive diarization performance.

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17640
• PDF: https://arxiv.org/pdf/2601.17640
• Github: https://github.com/usc-sail/joint-asr-diarization-child-adult

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#ASR #SpeakerDiarization #SpeechProcessing #DeepLearning #ChildAdultInteraction

✨Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices

📝 Summary:
Flavors of Moonshine are tiny monolingual ASR models for underrepresented languages. They outperform larger multilingual models by using balanced data, achieving 48% lower error rates. This enables accurate on-device speech recognition.

🔹 Publication Date: Published on Sep 2, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02523
• PDF: https://arxiv.org/pdf/2509.02523
• Github: https://github.com/moonshine-ai/moonshine

🔹 Models citing this paper:
• https://huggingface.co/UsefulSensors/moonshine-tiny-ja
• https://huggingface.co/UsefulSensors/moonshine-tiny-ar
• https://huggingface.co/UsefulSensors/moonshine-tiny-zh

✨ Spaces citing this paper:
• https://huggingface.co/spaces/wmoto-ai/moonshine-tiny-ja-demo

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#ASR #EdgeAI #LowResourceLanguages #MachineLearning #TinyML

✨Distilling Conversations: Abstract Compression of Conversational Audio Context for LLM-based ASR

📝 Summary:
LLM-based ASR improves with multimodal conversational context, especially for entities. Raw audio context is costly, so Abstract Compression replaces prior-turn audio with fixed latent tokens, retaining transcripts. This reduces computational cost while recovering some performance gains.

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26246
• PDF: https://arxiv.org/pdf/2603.26246

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#LLM #ASR #SpeechRecognition #NLP #AI