Methods › Audio › Text-to-Speech Models › FastSpeech 2

FastSpeech 2

20 papers tagged archive 2025-07-28

Introduced by Yi Ren et al. in FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

FastSpeech2 is a text-to-speech model that aims to improve upon FastSpeech by better solving the one-to-many mapping problem in TTS, i.e., multiple speech variations corresponding to the same text. It attempts to solve this problem by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, in FastSpeech 2, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs in training and use predicted values in inference.

The encoder converts the phoneme embedding sequence into the phoneme hidden sequence, and then the variance adaptor adds different variance information such as duration, pitch and energy into the hidden sequence, finally the mel-spectrogram decoder converts the adapted hidden sequence into mel-spectrogram sequence in parallel. FastSpeech 2 uses a feed-forward Transformer block, which is a stack of self-attention and 1D-convolution as in FastSpeech, as the basic structure for the encoder and mel-spectrogram decoder.

PaperSource

Papers archive 2025-07-28

20 shown of 20, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 36 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Text to Speech13
text-to-speech13
Speech Synthesis7
Text-To-Speech Synthesis5
Decoder2
Knowledge Distillation2
Speech-to-Speech Translation2
Speech-to-Text2
Translation2
All1
Audio Generation1
Chinese Word Segmentation1
Cross-Lingual Transfer1
Data Augmentation1
Multi-Task Learning1
Natural Language Understanding1
POS1
POS Tagging1
Part-Of-Speech Tagging1
Polyphone disambiguation1

Usage over time archive 2025-07-28

Papers per year tagged with FastSpeech 2: 2020 to 2025, peak 6 6 0 2020: 2 papers 2020 2021: 6 papers 2021 2022: 3 papers 2022 2023: 5 papers 2023 2024: 3 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (20 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Text-to-Speech Models

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