MOSS-TTS v1.5 is a speech synthesis model released by MOSI.AI and the OpenMOSS Team on May 26, 2026. It aims to handle not only standard text-to-speech conversion but also long-form narration, multi-speaker dialogue, multilingual synthesis, reference voice-based voice cloning, and character voice design within a single open model family. Rather than being an engine that simply reads text in a uniform voice, it is closer to an audio production model that constructs audio content by taking scripts and speaker information as input. In v1.5, improvements were introduced in synthesis using language tags, stability of cloned voices, prosody expression, and explicit pause control. Detailed model architecture, supported languages, and input formats must be verified via official documentation. While existing TTS systems are stable for short, single-speaker sentences, they often exhibit inconsistencies in voice or speech rhythm when handling long manuscripts. Additionally, multi-speaker dialogues frequently require assembling outputs from different models and post-processing steps. Voice cloning may also demand separate models or fine-tuning, complicating the content creation pipeline. The key differentiator of MOSS-TTS v1.5 is its ability to connect general synthesis, reference voice cloning, multi-speaker dialogue, and multilingual output within the same model family, allowing users to explicitly define the structure of the result through language tags and pause control. Similar to how GPT constructs text for multiple speakers within a single context, MOSS-TTS can be viewed as a tool that translates script information regarding speakers, languages, and pauses into a coherent audio flow. Life science researchers can convert paper abstracts or experimental protocols into long-form audio for review on the go. For example, by assigning language tags to Korean and English scripts generated by a paper summarization model and inserting explicit pauses at section boundaries, researchers can produce structured audio briefings containing specialized terminology. Supported languages, tag syntax, maximum input length, and quantitative metrics for voice consistency in long-form synthesis should be further verified in the official documentation. In patient education or research participant guidance materials, sentences for counselors and participants can be assigned to different speakers, and reference voice-based voice cloning or character voice design features can be applied to create interactive guidance content. However, when deploying in actual clinical environments, separate policies must be established regarding voice usage consent, prevention of identity theft, handling of sensitive information, and disclosure of generated voices. While real-time synthesis and environmental sound generation are also introduced as part of the MOSS-TTS open model family, the exact features, latency, and hardware-specific throughput provided in the v1.5 single checkpoint lack confirmed official figures and require additional verification.

MOSS-TTS v1.5 is a text-to-speech model released by MOSI.AI and the OpenMOSS Team on May 26, 2026. It aims to handle not only standard text-to-speech but also long-form narration, multi-speaker dialogue, multilingual synthesis, reference-voice-based voice cloning, and character voice design within a single open-source model family. Rather than being an engine that simply reads text in a uniform voice, it is closer to an audio production model that constructs speech content by taking scripts and speaker information as input. In v1.5, improvements include synthesis using language tags, stability of cloned voices, prosody expression, and explicit pause control.

🔬 Multilingual Paper Audio Briefing: Generate long-form briefings by applying MOSS-TTS v1.5 language tags and section-specific pauses to Korean and English scripts created by the paper summarization model. Quantitative results, such as maximum input length and word error rate, should be documented after verifying against official benchmarks.