Speaking style adaption in text-to-speech synthesis usng sequence-to-sequence models with attention
Currently, there are increasing interests in text-to-speech (TTS) syn- thesis to use sequence-to-sequence models with attention. These models are end-to-end meaning that they learn both co-articulation and duration properties directly from text and speech. Since these models are entirely data-driven, they need large amounts of data to generate synthetic speech with good quality. However, in chal- lenging speaking styles, such as Lombard speech, it is difficult to record sufficiently large speech corpora. Therefore, in this study we propose a transfer learning method to adapt a sequence-to-sequence based TTS system of normal speaking style to Lombard style. More- over, we experiment with a WaveNet vocoder in synthesis of Lom- bard speech. We conducted subjective evaluations to assess the per- formance of the adapted TTS systems. The subjective evaluation re- sults indicated that an adaptation system with the WaveNet vocoder clearly outperformed the conventional deep neural network based TTS system in synthesis of Lombard speech.
The results of the style similarity test are plotted in Figure 4. From the right pane of the figure, it can be observed that synthesized speech by all adapted systems were rated to sound different from natural normal speech with high confidence. When compared to the natural Lombard reference (left pane), system S5 was rated highest, followed by systems S3, S4, S2, and S1. System S5 was built us- ing the Seq2Seq-TTS model and the mel-spectrogram as output. It can be clearly seen that those systems that employed the WaveNet vocoder got higher scores than the ones that used the more tradi- tional World vocoder. Further, system S5, which is based on con- ditioning the WaveNet vocoder with mel-spectrograms, got a higher score than the ones that used the World vocoder, confirming the find- ings made in a earlier study . From this results we can conclude that even though we trained the WaveNet vocoder with only 30 min- utes of Lombard speech, system S5 generated synthetic speech that was most Lombard-like among the systems compared.
This paper compared different TTS models and vocoders to adapt the speaking style of speech synthesis from normal to Lombard. The study proposes using an adaptation method based on fine-tuning combined with sequence-to-sequence based TTS models and the WaveNet vocoder conditioned using mel-spectrograms. Listening tests show that the proposed method outperformed the previous best method that was developed using a LSTM-RNN based adapted sys- tem. Future work includes an extensive subjective evaluations and training both the WaveNet and Seq2Seq-TTS model in a single pipeline.
FastSpeech: Fast, Robust and Controllable speech
Abstract Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from the mel-spectrogram using vocoder such as WaveNet. Compared with traditional concatenative and statistical parametric approaches, neural network based end- to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i.e., some words are skipped or repeated) and lack of con- trollability (voice speed or prosody control). In this work, we propose a novel feed-forward network based on Transformer to generate mel-spectrogram in paral- lel for TTS. Specifically, we extract attention alignments from an encoder-decoder based teacher model for phoneme duration prediction, which is used by a length regulator to expand the source phoneme sequence to match the length of the target mel-spectrogram sequence for parallel mel-spectrogram generation. Experiments on the LJSpeech dataset show that our parallel model matches autoregressive mod- els in terms of speech quality, nearly eliminates the problem of word skipping and repeating in particularly hard cases, and can adjust voice speed smoothly. Most importantly, compared with autoregressive Transformer TTS, our model speeds up mel-spectrogram generation by 270x and the end-to-end speech synthesis by 38x. Therefore, we call our model FastSpeech. We will release the code on Github.3
Text to Speech TTS [1, 16, 19, 20, 24], which aims to synthesize natural and intelligible speech given text, has long been a hot research topic in the field of artificial intelligence. The research on TTS has shifted from early concatenative synthesis , statistical parametric synthesis [12, 25] to neural network based parametric synthesis  and end-to-end models [13, 16, 20, 24], and the quality of the synthesized speech by end-to-end models is close to human parity. Neural network based end-to-end TTS models usually first convert the text to acoustic features (e.g., mel-spectrograms) and then transform mel-spectrograms into audio samples. However, most neural TTS systems generate mel-spectrograms autoregressively, which suffers from slow inference speed, and synthesized speech usually lacks of robustness (word skipping and repeating) and controllability (voice speed or prosody control). In this work, we propose FastSpeech to generate mel-spectrograms non-autoregressively, which sufficiently handles the above problems.
Sequence to Sequence Learning Sequence to sequence learning [2, 4, 22] is usually built on the encoder-decoder framework: The encoder takes the source sequence as input and generates a set of representations. After that, the decoder estimates the conditional probability of each target element given the source representations and its preceding elements. The attention mechanism  is further introduced between the encoder and decoder in order to find which source representations to focus on when predicting the current element, and is an important component for sequence to sequence learning. In this work, instead of using the conventional encoder-attention-decoder framework for sequence to sequence learning, we propose a feed-forward network to generate a sequence in parallel.
Fast Speech Architecture:
In this section, we introduce the architecture design of FastSpeech. To generate a target mel- spectrogram sequence in parallel, we design a novel feed-forward structure, instead of using the encoder-attention-decoder based architecture as adopted by most sequence to sequence based autore- gressive [13, 20, 22] and non-autoregressive [7, 8, 23] generation. The overall model architecture of FastSpeech is shown:
a) FeedForward Transformer b) Length Regulator c) Duration Predictor
The architecture for FastSpeech is a feed-forward structure based on self-attention in Transformer  and 1D convolution [5, 17]. We call this structure as Feed-Forward Transformer (FFT), as shown in Figure 1a. Feed-Forward Transformer stacks multiple FFT blocks for phoneme to mel-spectrogram transformation, with N blocks on the phoneme side, and N blocks on the mel-spectrogram side, with a length regulator (which will be described in the next subsection) in between to bridge the length gap between the phoneme and mel-spectrogram sequence. Each FFT block consists of a self-attention and 1D convolutional network, as shown in Figure 1b. The self-attention network consists of a multi-head attention to extract the cross-position information. Different from the 2-layer dense network in Transformer, we use a 2-layer 1D convolutional network with ReLU activation. The motivation is that the adjacent hidden states are more closely related in the character/phoneme and mel-spectrogram sequence in speech tasks. We evaluate the effectiveness of the 1D convolutional network in the experimental section. Following Transformer , residual connections, layer normalization, and dropout are added after the self-attention network and 1D convolutional network respectively.
The length regulator (Figure 1c) is used to solve the problem of length mismatch between the phoneme and spectrogram sequence in the Feed-Forward Transformer, as well as to control the voice speed and part of prosody. The length of a phoneme sequence is usually smaller than that of its mel-spectrogram sequence, and each phoneme corresponds to several mel-spectrograms. We refer to the length of the mel-spectrograms that corresponds to a phoneme as the phoneme duration (we will describe how to predict phoneme duration in the next subsection). Based on the phoneme duration d, the length regulator expands the hidden states of the phoneme sequence d times, and then the total length of the hidden states equals the length of the mel-spectrograms.
Phoneme duration prediction is important for the length regulator. As shown in Figure 1d, the duration predictor consists of a 2-layer 1D convolutional network with ReLU activation, each followed by the layer normalization and the dropout layer, and an extra linear layer to output a scalar, which is exactly the predicted phoneme duration. Note that this module is stacked on top of the FFT blocks on the phoneme side and is jointly trained with the FastSpeech model to predict the length of mel-spectrograms for each phoneme with the mean square error (MSE) loss.