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Talent Scout

Y

Yisi Liu

UC Berkeley

N

Nicholas Lee

UC Berkeley

G

Gopala Anumanchipalli

UC Berkeley

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References (67)

[1]
IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech
2025Siyi Zhou, Yiquan Zhou et al.
[2]
RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding
2025Yisi Liu, Chenyang Wang et al.
[3]
CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
2025Zhihao Du, Changfeng Gao et al.
[4]
Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement
2025Xueyao Zhang, Xiaohui Zhang et al.
[5]
Overview of the Amphion Toolkit (v0.2)
2025Jiaqi Li, Xueyao Zhang et al.
[6]
CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models
2024Zhihao Du, Yuxuan Wang et al.
[7]
Zero-shot Voice Conversion with Diffusion Transformers
2024Songting Liu
[8]
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
2024Yushen Chen, Zhikang Niu et al.
[9]
Fast, High-Quality and Parameter-Efficient Articulatory Synthesis Using Differentiable DSP
2024Yisi Liu, Bohan Yu et al.
[10]
MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer
2024Yuancheng Wang, Haoyue Zhan et al.
[11]
Convert and Speak: Zero-shot Accent Conversion with Minimum Supervision
2024Z. Jia, Huaying Xue et al.
[12]
CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens
2024Zhihao Du, Qian Chen et al.
[13]
Emilia: An Extensive, Multilingual, and Diverse Speech Dataset For Large-Scale Speech Generation
2024Haorui He, Zengqiang Shang et al.
[14]
E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
2024S. Eskimez, Xiaofei Wang et al.
[15]
GLOBE: A High-quality English Corpus with Global Accents for Zero-shot Speaker Adaptive Text-to-Speech
2024Wenbin Wang, Yang Song et al.
[16]
Coding Speech Through Vocal Tract Kinematics
2024Cheol Jun Cho, Peter Wu et al.
[17]
DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion
2024Ziqian Ning, Shuai Wang et al.
[18]
Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
2024Philip Anastassiou, Jiawei Chen et al.
[19]
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
2024Zeqian Ju, Yuancheng Wang et al.
[20]
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
2024Mateusz Lajszczak, Guillermo Cámbara et al.

Showing 20 of 67 references

Founder's Pitch

"StyleStream enables real-time zero-shot voice style conversion across timbre, accent, and emotion."

Speech ProcessingScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

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Why It Matters

Real-time voice style conversion can revolutionize voice services by enabling dynamic personalization, telecommunication clarity in diverse accents, and emotional context in virtual assistants.

Product Angle

Develop and market an API for real-time voice style conversion, targeting entertainment and telecommunications industries for dynamic voice personalization.

Disruption

This technology could replace existing voice cloning or stylistic transformation tools that only modify single attributes or require prior training data, allowing seamless integration in live communication applications.

Product Opportunity

The gaming and streaming markets are steadily growing with millions of users who may pay for real-time persona establishment tools; telecoms might also benefit from enhancing communication clarity across accents.

Use Case Idea

Create an API for video gamers and streamers to modify their voice style in real-time, enhancing their online persona and interaction with their audience.

Science

The system uses a two-part architecture. The Destylizer strips away style attributes from speech to preserve linguistic content, while the Stylizer reintroduces target style characteristics through a diffusion transformer model, supporting real-time conversion achieved by a 1-second end-to-end latency.

Method & Eval

Tested against existing benchmarks in voice style conversion, StyleStream showed state-of-the-art performance in converting voice styles with accuracy in timbre, accent, and emotion matching while maintaining linguistic integrity.

Caveats

The system currently relies on English language data, and its performance in other languages is unverified. The technology might also require significant optimization to handle noisy environments robustly.

Author Intelligence

Yisi Liu

UC Berkeley
louis liu@berkeley.edu

Nicholas Lee

UC Berkeley

Gopala Anumanchipalli

UC Berkeley