# Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis

**Authors:** Jungil Kong, Junmo Lee, Jeongmin Kim, Beomjeong Kim, Jihoon Park, Dohee Kong, Changheon Lee, Sangjin Kim  
**arXiv:** [2311.11745](https://arxiv.org/abs/2311.11745)  
**Date:** 2023-11-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.11745v1/

## Key points

- Authors propose a novel method for modeling numerous speakers
- Method allows for expressing overall characteristics of speakers in detail without additional training on target speaker's dataset
- Approach captures speaker-specific latent speech features through feature learning and discretization techniques
- Outperforms existing methods in subjective similarity evaluation
- Surpasses zero-shot methods in generating new artificial speakers
- Encoded latent features are informative enough to completely reconstruct an original speaker's speech

## Summary

In their work titled "Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis," authors Jungil Kong, Junmo Lee, Jeongmin Kim, Beomjeong Kim, Jihoon Park, Dohee Kong, Changheon Lee, and Sangjin Kim propose a novel method for modeling numerous speakers. This method allows for expressing the overall characteristics of speakers in detail similar to a trained multi-speaker model without the need for additional training on the target speaker's dataset. The proposed approach effectively captures speaker-specific latent speech features through feature learning and discretization techniques. It outperforms existing methods in subjective similarity evaluation and surpasses zero-shot methods in generating new artificial speakers. The encoded latent features are informative enough to completely reconstruct an original speaker's speech, making this method applicable across various tasks. This research presents a promising approach towards enhancing speech synthesis applications by incorporating .