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## Available Information
- **Title:** “Beyond AI That Speaks Well: Making Kanana-o Speak the Way Users Want”
- **Topic indicated by the title:** Improving Kanana-o’s voice-generation capabilities to produce speech according to user preferences.
- **Authors:** martin.gale, abigail.r, and edwin.ai
- **Missing:** The article’s main sections, implementation details, experiments, and conclusions.
Please provide the full article text or its URL content for a detailed summary.
Kanana-o’s latest speech-generation improvements target two goals: faster, more efficient synthesis and more precise adherence to user instructions. Kakao addresses these through LM-SPT, a speech tokenizer that separates semantic and acoustic information while reducing the token rate from 25 Hz to 12.5 Hz, and through online reinforcement learning. Together, these changes enable Kanana-o to generate natural speech more efficiently and control characteristics such as speed, pitch, tone, and volume more reliably.
## Goals for Kanana-o’s Speech Generation
- Improve real-time performance by shortening speech-token sequences and simplifying decoding.
- Move beyond merely natural speech toward speech that follows explicit user preferences.
- Support control over:
- Speaking speed
- Voice quality and tone
- Pitch and intonation
- Volume
- Emotional and conversational style
- Combine better speech representation with instruction-following training.
## Limitations of the Original System
- The original Kanana-o represented speech with 25 discrete tokens per second.
- Its Voice Token LM generated these tokens sequentially based on text responses and conversation context.
- Speech reconstruction required two stages:
- **Token-to-Mel:** Convert speech tokens into a mel-spectrogram.
- **Mel-to-Waveform:** Convert the mel-spectrogram into a waveform.
- The tokenizer captured linguistic content effectively but did not explicitly represent acoustic properties such as voice timbre, pitch, intonation, speed, or emotion.
- Long token sequences increased generation latency and computational cost.
- The two-stage decoder, often involving iterative diffusion or flow-matching inference, made the pipeline difficult to optimize for real-time services.
## LM-SPT: A More Efficient Speech Tokenizer
- LM-SPT stands for **LM-aligned SPeech Tokenizer**.
- It compresses speech to 12.5 frames per second—half the original rate—reducing the number of sequential prediction steps.
- It represents both:
- **Semantic speech tokens:** The spoken content aligned with text and conversational context.
- **Acoustic speech tokens:** Voice-specific details such as timbre, pitch, intonation, and speaking rate.
- This separation allows the language model to generate content and acoustic characteristics more independently and controllably.
- LM-SPT uses:
- Two encoders for semantic and acoustic information
- One semantic codebook
- Multiple acoustic codebooks
- A Split Residual Vector Quantization structure
## Semantic Speech-Resynthesis Distillation
- Training only for waveform reconstruction does not guarantee that semantic and acoustic information remain separated.
- Earlier systems commonly distilled representations from self-supervised models such as HuBERT or WavLM.
- That approach can suffer from:
- Misalignment between phonetic representations and higher-level language-model semantics
- Loss of information when matching models with different frame rates
- LM-SPT instead uses a **Semantic Speech-Resynthesis Distillation** method:
- Reconstruct speech using only semantic tokens.
- Compare the original and reconstructed speech with a pretrained speech encoder aligned to language-model representations.
- Train the semantic tokens to preserve the same meaning without requiring exact frame-by-frame teacher alignment.
- This approach helps retain meaningful content even at the lower 12.5 Hz token rate.
## Simplified Speech Decoding
- During normal tokenization and reconstruction, the system does not require a heavy pretrained speech encoder.
- A lightweight learned encoder is sufficient.
- The final decoder uses semantic and acoustic tokens together to reconstruct the waveform directly.
- This removes the intermediate mel-spectrogram stage and replaces the previous two-stage process with a lighter single-decoder structure.
- As a result, the system reduces both language-model generation length and waveform reconstruction complexity.
## Instruction Following Through Online Reinforcement Learning
- LM-SPT provides the representation needed to control acoustic features at the token level.
- Kanana-o also applies online reinforcement learning to teach the speech-generation module to follow diverse vocal instructions.
- The objective is to balance:
- Accurate compliance with requested speaking styles
- Natural and high-quality audio output
Kakao’s approach combines a lower-rate, semantically and acoustically structured tokenizer with reinforcement learning for instruction adherence. The result is intended to make Kanana-o faster and more suitable for real-time use while allowing users to specify not only what the system says, but how it says it.
Kanana-o is Kakao’s new Korean-focused omni-modal AI model, designed to understand and generate text, images, and audio naturally. Kakao is opening a closed beta for the Kanana-1.5-o-9.8b-2602 model to gather feedback from developers and partners before commercial release. The service emphasizes practical experimentation rather than large-scale traffic handling.
## Model Capabilities
- Supports simultaneous processing of multiple modalities, including text, images, and audio.
- Specializes in:
- Deep understanding of Korean language, culture, and user intent.
- Natural Korean speech with expressive intonation, pacing, and emotion.
- Flexible applications such as podcast narration, multi-turn conversations, and multi-speaker text-to-speech.
- Balances text-generation speed with audio-processing speed to produce more natural spoken responses.
## API Beta Service
- **Service:** Kanana-o API Beta
- **Model:** Kanana-1.5-o-9.8b-2602
- **Beta period:** February 27–May 27, 2026
- **Access:** Selected testers receive a fixed number of daily API uses during the beta.
- The closed beta is intended for meaningful developer testing and feedback, not high-volume production workloads.
## Application and Selection
- Applicants should visit [omni.kanana.ai](https://omni.kanana.ai/), sign in with a Kakao account, and submit information about:
- Their organization or affiliation
- Intended purpose
- Expected technical scenarios
- Selected applicants will receive invitations and API documentation through KakaoTalk notifications starting February 27.
- Kakao is seeking developers, students, startups, and researchers with concrete implementation plans.
- Specific proposals—such as building a visual shopping assistant for people with visual impairments—are favored over general interest in trying AI.
Developers interested in exploring Korean-language, audio, and vision applications can apply for the beta with a clearly defined use case and prototype plan.
