FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
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Abstract
Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duration prediction (to provide more information as input) and knowledge distillation (to simplify the data distribution in output), which can ease the one-to-many mapping problem (i.e., multiple speech variations correspond to the same text) in TTS. However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated and time-consuming, 2) the duration extracted from the teacher model is not accurate enough, and the target…
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1Topics & keywords
Topics
Keywords
- Computer science
- Autoregressive model
- Speech recognition
- Inference
- Waveform
- Spectrogram
- Speech synthesis
- Duration (music)
UN Sustainable Development Goals
- Quality Education
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