Search: speech-to-speech - ai.jp.net

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:01

From Signal to Turn: Interactional Friction in Modular Speech-to-Speech Pipelines

Published:Dec 12, 2025 17:05

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1 min read

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ArXiv

Analysis

This article likely analyzes the challenges of building speech-to-speech systems, focusing on the difficulties that arise when different modules interact. The term "interactional friction" suggests a focus on the practical problems of integrating these modules, potentially including latency, errors, and the overall smoothness of the conversation.

Key Takeaways

Reference

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Permalink ArXiv

Research #Translation 🔬 ResearchAnalyzed: Jan 10, 2026 14:17

RosettaSpeech: Groundbreaking Zero-Shot Speech Translation from Monolingual Data

Published:Nov 26, 2025 02:02

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1 min read

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ArXiv

Analysis

This research explores a novel approach to speech-to-speech translation leveraging monolingual data in a zero-shot manner. The ability to translate between languages without parallel data could significantly advance accessibility and cross-cultural communication.

Key Takeaways

•RosettaSpeech enables speech translation without relying on parallel data.
•The approach uses monolingual data for training, potentially overcoming data scarcity issues.
•This work addresses a critical challenge in machine translation: the need for paired data.

Reference

“RosettaSpeech performs zero-shot speech-to-speech translation.”

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Research #Translation 🔬 ResearchAnalyzed: Jan 10, 2026 14:43

Boosting Persian-English Speech Translation: Discrete Units & Synthetic Data

Published:Nov 16, 2025 17:14

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1 min read

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ArXiv

Analysis

This research explores enhancements to direct speech-to-speech translation between Persian and English, a valuable contribution given the limited resources available for these language pairs. The use of discrete units and synthetic parallel data are promising approaches to improving performance, potentially benefiting wider accessibility of information.

Key Takeaways

•Addresses challenges in translating low-resource language pairs.
•Employs discrete units and synthetic data for enhanced performance.
•Aims to improve direct speech-to-speech translation quality.

Reference

“The research focuses on improving direct Persian-English speech-to-speech translation.”

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Research #llm 📝 BlogAnalyzed: Dec 29, 2025 06:05

Multimodal AI on Apple Silicon with MLX: An Interview with Prince Canuma

Published:Aug 26, 2025 16:55

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1 min read

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Practical AI

Analysis

This article summarizes an interview with Prince Canuma, an ML engineer and open-source developer, focusing on optimizing AI inference on Apple Silicon. The discussion centers around his contributions to the MLX ecosystem, including over 1,000 models and libraries. The interview covers his workflow for adapting models, the trade-offs between GPU and Neural Engine, optimization techniques like pruning and quantization, and his work on "Fusion" for combining model behaviors. It also highlights his packages like MLX-Audio and MLX-VLM, and introduces Marvis, a real-time speech-to-speech voice agent. The article concludes with Canuma's vision for the future of AI, emphasizing "media models".

Key Takeaways

•Prince Canuma is a key contributor to the MLX ecosystem, making multimodal AI accessible on Apple devices.
•The interview explores practical aspects of optimizing AI models for Apple Silicon, including performance trade-offs and optimization techniques.
•The future of AI is envisioned to be centered around "media models" capable of handling multiple modalities.

Reference

“Prince shares his journey to becoming one of the most prolific contributors to Apple’s MLX ecosystem.”

Permalink Practical AI

Technology #AI, Embedded Systems, Open Source 👥 CommunityAnalyzed: Jan 3, 2026 16:15

Open-Source AI Speech Companion on ESP32

Published:Apr 22, 2025 14:10

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1 min read

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Hacker News

Analysis

This Hacker News post announces the open-sourcing of a project that creates a real-time AI speech companion using an ESP32-S3 microcontroller, OpenAI's Realtime API, and other technologies. The project aims to provide a user-friendly speech-to-speech experience, addressing the lack of readily available solutions for secure WebSocket-based AI services. The project's focus on low latency and global connectivity using edge servers is noteworthy.

Key Takeaways

•Open-source project for real-time AI speech companion.
•Utilizes ESP32-S3, OpenAI Realtime API, and other technologies.
•Focuses on secure WebSockets and low-latency communication.
•Addresses the lack of user-friendly speech-to-speech solutions.

Reference

“The project addresses the lack of beginner-friendly solutions for secure WebSocket-based AI speech services, aiming to provide a great speech-to-speech experience on Arduino with Secure Websockets using Edge Servers.”

Permalink Hacker News

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 05:56

Deploying Speech-to-Speech on Hugging Face

Published:Oct 22, 2024 00:00

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1 min read

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Hugging Face

Analysis

This article likely discusses the process of deploying speech-to-speech models on the Hugging Face platform. It would cover technical aspects like model selection, deployment strategies, and potential use cases. The source, Hugging Face, suggests it's an official guide or announcement.

Key Takeaways

•Focus on deploying speech-to-speech models.
•Likely provides technical details on deployment.
•Published by Hugging Face, indicating official guidance.

Reference

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Permalink Hugging Face

From Signal to Turn: Interactional Friction in Modular Speech-to-Speech Pipelines

Analysis

Key Takeaways

RosettaSpeech: Groundbreaking Zero-Shot Speech Translation from Monolingual Data

Analysis

Key Takeaways

Boosting Persian-English Speech Translation: Discrete Units & Synthetic Data

Analysis

Key Takeaways

Multimodal AI on Apple Silicon with MLX: An Interview with Prince Canuma

Analysis

Key Takeaways

Open-Source AI Speech Companion on ESP32

Analysis

Key Takeaways

Deploying Speech-to-Speech on Hugging Face

Analysis

Key Takeaways

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