Text to speech (TTS) is widely used to synthesize personal voice for a target
speaker, where a well-trained source TTS model is fine-tuned with few paired
adaptation data (speech and its transcripts) on this target speaker. However,
in many scenarios, only untranscribed speech data is available for adaptation,
which brings challenges to the previous TTS adaptation pipelines (e.g.,
AdaSpeech). In this paper, we develop AdaSpeech 2, an adaptive TTS system that
only leverages untranscribed speech data for adaptation. Specifically, we
introduce a mel-spectrogram encoder to a well-trained TTS model to conduct
speech reconstruction, and at the same time constrain the output sequence of
the mel-spectrogram encoder to be close to that of the original phoneme
encoder. In adaptation, we use untranscribed speech data for speech
reconstruction and only fine-tune the TTS decoder. AdaSpeech 2 has two
advantages: 1) Pluggable: our system can be easily applied to existing trained
TTS models without re-training. 2) Effective: our system achieves on-par voice
quality with the transcribed TTS adaptation (e.g., AdaSpeech) with the same
amount of untranscribed data, and achieves better voice quality than previous
untranscribed adaptation methods. Synthesized speech samples can be found at
this https URL

本文提出了 AdaSpeech 2，这是一个自适应 TTS 系统，该系统仅利用未经转录的语音数据进行适应。通过引入 mel 频谱编码器进行语音重构，并将其输出序列约束为原始音素编码器的输出序列，然后仅微调 TTS 解码器。AdaSpeech 2 有两个优点：1）可插拔：我们的系统可以轻松应用于现有训练过的 TTS 模型而无需重新训练。2）有效：我们的系统以与经过转录的 TTS 适应相同数量的未经转录数据实现同等语音质量，并实现比以前的未经转录适应方法更好的语音质量。

AdaSpeech 2: 自适应文本转语音技术在无文字数据上的应用

AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data

We present a meta-learning approach for adaptive text-to-speech (TTS) with
few data. During training, we learn a multi-speaker model using a shared
conditional WaveNet core and independent learned embeddings for each speaker.
The aim of training is not to produce a neural network with fixed weights,
which is then deployed as a TTS system. Instead, the aim is to produce a
network that requires few data at deployment time to rapidly adapt to new
speakers. We introduce and benchmark three strategies: (i) learning the speaker
embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire
architecture with stochastic gradient descent, and (iii) predicting the speaker
embedding with a trained neural network encoder. The experiments show that
these approaches are successful at adapting the multi-speaker neural network to
new speakers, obtaining state-of-the-art results in both sample naturalness and
voice similarity with merely a few minutes of audio data from new speakers.

采用元学习方法对自适应文本到语音合成进行研究，通过共享 WaveNet 核和独立学习的扬声器嵌入来学习多讲话者模型，该方法能够成功地将多扬声器神经网络进行快速适应并获得最新演讲者的自然音质和语音相似度的最新结果。