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New TTS models achieve lower latency and improved speech-text alignment · 4 sources tracked

By PulseAugur Editorial · [6 sources] ·

Researchers have developed new methods for improving text-to-speech (TTS) systems, focusing on achieving lower latency and better alignment between text and speech. CTC-TTS utilizes a CTC-based aligner and a bi-word interleaving strategy to outperform traditional methods in streaming synthesis. StreamAlign enables real-time streaming tokenization by combining character-level RNN-Transducer alignment with word-level ASR guidance, significantly reducing latency. TontaubeV1 employs a hierarchical codec modeling approach with bounded context, separating semantic and acoustic streams to maintain natural prosody while enabling efficient streaming inference on consumer GPUs. AI

IMPACT Advances in streaming TTS could enable more responsive and natural voice interactions in real-time applications.

RANK_REASON Multiple research papers detailing new models and techniques for text-to-speech synthesis.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New TTS models achieve lower latency and improved speech-text alignment · 4 sources tracked

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Multiple research papers detailing new models and techniques for text-to-speech synthesis.
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6 independent sources
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model release, paper, infra
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COVERAGE [6]

  1. arXiv cs.CL TIER_1 English(EN) · Roman Koshkin, Jeon Haesung, Lianbo Liu, Hao Shi, Mengjie Zhao, Yusuke Fujita, Yui Sudo ·

    Streaming Translation and Transcription Through Speech-to-Text Causal Alignment

    arXiv:2603.11578v2 Announce Type: replace Abstract: Simultaneous machine translation (SiMT) has traditionally relied on offline machine translation models coupled with human-engineered heuristics or learned policies. We propose Hikari, a policy-free, end-to-end model for simultan…

  2. arXiv cs.AI TIER_1 English(EN) · Hanwen Liu, Saierdaer Yusuyin, Hao Huang, Zhijian Ou ·

    CTC-TTS: LLM-based dual-streaming text-to-speech with CTC alignment

    arXiv:2602.19574v2 Announce Type: replace-cross Abstract: Large-language-model (LLM)-based text-to-speech (TTS) systems can generate natural speech, but most are not designed for low-latency dual-streaming synthesis. High-quality dual-streaming TTS depends on accurate text--speec…

  3. arXiv cs.CL TIER_1 English(EN) · Kang-wook Kim, Jinyoung Park, Jinsoo Kim, Sehun Lee, Sang Hoon Woo, Gunhee Kim ·

    StreamAlign: Streaming Text-Aligned Speech Tokenization

    arXiv:2609.09719v1 Announce Type: new Abstract: Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretrained LLMs. However, they rely on offline automatic speech recognition (ASR), lea…

  4. arXiv cs.LG TIER_1 English(EN) · Fritz Cremer, Jonathan Cremer ·

    TontaubeV1: Streaming Text-to-Speech with Hierarchical Codec Modeling and Bounded Context

    arXiv:2609.08703v1 Announce Type: cross Abstract: Text-to-speech systems often face a trade-off between natural prosody and efficient inference: higher perceptual quality typically comes at increased computational cost and latency. We present TontaubeV1, a model that preserves na…

  5. Hugging Face Daily Papers TIER_1 English(EN) ·

    StreamAlign: Streaming Text-Aligned Speech Tokenization

    Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretrained LLMs. However, they rely on offline automatic speech recognition (ASR), leading to two key limitations: (i) the need for co…

  6. Hugging Face Daily Papers TIER_1 English(EN) ·

    TontaubeV1: Streaming Text-to-Speech with Hierarchical Codec Modeling and Bounded Context

    Text-to-speech systems often face a trade-off between natural prosody and efficient inference: higher perceptual quality typically comes at increased computational cost and latency. We present TontaubeV1, a model that preserves natural prosody while enabling streaming from a sing…

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