# Audio Samples from "E3-VITS: Emotional End-to-End TTS with Cross-speaker Style Transfer"

Paper: E3-VITS: Emotional End-to-End TTS with Cross-speaker Style Transfer

Abstract: Since previous emotional TTS models are based on a two-stage pipeline or additional labels, their training process is complex and requires a high labeling cost. To deal with this problem, this paper presents E3-VITS, an end-to-end emotional TTS model that addresses the limitations of existing models. E3-VITS synthesizes high-quality speeches for multi-speaker conditions, supports both reference speech and textual description-based emotional speech synthesis, and enables cross-speaker emotion transfer with a disjoint dataset. To implement E3-VITS, we propose batch-permuted style perturbation, which generates audio samples with unpaired emotion to increase the quality of cross-speaker emotion transfer. Results show that E3-VITS outperforms the baseline model in terms of naturalness, speaker and emotion similarity, and inference speed.

## 1. Ground Truth Samples

We utilize FSNR0 Korean Style Tagging TTS dataset which includes speech recordings, transcriptions, emotional category, and style tags.
These are the speech recordings with their correspoding emotional categories and Korean style tags. We add English translations of style tags for understanding.

#### Neutral source dataset samples

Emotional categories: NEUTRAL
Style tag : #지문(normally) | Style tag : #지문(normally)

#### Emotional target dataset samples

Emotional categories: ANGRY
Style tag : #짜증난듯(annoyed) | Style tag : #화가난듯(seem angry)
Emotional categories: JOY
Style tag : #행복한듯(happy) | Style tag : #반가운듯(welcoming)
Emotional categories: SAD
Style tag : #억울한듯(feeling unfair) | Style tag : #울먹이듯(about to cry)

## 2. Style Tag-based Style Transfer

- The "Seen" column contains audio samples from emotional target speakers, and the "Unseen" column contains audio samples from neutral source speakers.
- The

  blue tags

  are tags in the dataset, and the

  red tags

  are newly created tags, which are not in the dataset.

No. | Seen | Unseen
--- | --- | ---
1 | #기진맥진한듯 (exhausted) | #덤덤하게 (flat) | #힘없이 (weakly) | #큰소리로 (louldy) | #냉정하게 (coldly) | #힘없이 (weakly)
#친절하게 (kindly) | #다그치긋 (urging) | #정답게 (warmly) | #기쁜듯 (happy) | #정색하며 (straight face) | #활기찬 (lusty)
2 | #놀란듯 (surprised) | #너그러운 (generous) | #낙담한듯 (disappointed) | #신기한듯 (interesting) | #악을쓰듯 (shouting) | #높은 목소리로 (high-pitched voice)
#불편한듯 (uncomfortable) | #수긍하듯 (agreeing) | #혼란스러운듯 (confused) | #억울한듯 (feeling unfair) | #울먹이듯 (about to cry) | #낮은 목소리로 (low-pitched voice)

## 3. Reference Speech-based Style Transfer

- The "reference" samples are used as reference speeches from FSNR0, and the "synthesized" samples are generated with style embedding from the reference speeches of correspoding number.
- We attach style tags of reference speeches to provide more information to listeners. They were not used in actual speech systhesis.

No. | Seen | Unseen
--- | --- | ---
1 | reference 1 #화가난듯 (seem angry) | reference 2 #즐거운듯 (joyful) | reference 3 #차분하게 (calm) | reference 1 #빈정거리듯 (sarcastic) | reference 2 #싫은듯 (disliking) | reference 3 #무서운듯 (scared)
synthesized 1 | synthesized 2 | synthesized 3 | synthesized 1 | synthesized 2 | synthesized 3