SlowGuess/NeurIPS_2023_OCR huggingface dataset:
- Sounds great. At most points the differences were negligible.
- I really liked the person who was reading the clips.  
She sounds professional and was very good at changing her tone of voice. Sometimes it was hard to evaluate the clip because you had to listen carefully when they sounded similar.  
I didn't have any problems. I enjoyed participating Thank you!
- For most of the rounds, it was difficult to notice a difference between the voices.
- Interested to see the results - differences were sometimes noticeable, often subtle
This feedback strengthens our conclusion that StyleTTS 2 has reached human-like text-to-speech synthesis quality. Several participants offered specific observations regarding the differences:
- Some sounded a little unnatural because they sounded like an actor auditioning for a role - a little too dramatic.
- So, a couple of comments. I feel like the expressiveness of the speaker made me think it was more natural, but hyper expressiveness would be unnatural in certain contexts (and did feel odd with some of them but it felt more real). Additionally, the different voices, in being expressive, sometimes tended to turn into different emotions and contextual these emotions sometimes felt unnatural but felt real at the same time.
Fun study though. I wish you all well!
- some sound relatively unexpected/unnatural, but most often it was just a minor difference
These comments suggest that the differences between StyleTTS 2 and the original audio mainly lie in subtle nuances like intonation and word emphasis.
Our results indicate that StyleTTS 2 scored statistically higher than the original audio in both MOS and CMOS, likely due to the absence of contextual continuity in the common evaluation procedure used in TTS studies. This disruption of narrative continuity in the LJSpeech dataset, which consists of isolated audiobook clips, might have led to the perceived unnaturalness of the ground truth compared to samples generated by StyleTTS 2. Future research should aim to incorporate context-aware long-form generation into human-like text-to-speech synthesis to improve evaluation fairness and relevance.
A.2 Speech Expressiveness
Our model's ability to synthesize diverse and expressive speech is demonstrated by synthesizing speech from 500 text samples across five emotions generated using GPT-4. This reinforces the results visualized in Figure 2, which maps style vectors from texts associated with different emotions using t-SNE. We use the mean value of the F0 and energy curves to evaluate emotive speech.
As depicted in Figure 3 (a), speech synthesized by our model exhibits visible F0 and energy characteristics for each emotion, notably for anger and surprise texts, which deviate from the ground truth average. This substantiates our model's capacity to generate emotive speech from texts associated with specific emotions. Conversely, VITS shows certain degrees of insensitivity to emotional variance,
(a) StyleTTS2
(b) VITS
(c) JETS
Figure 3: Histograms and kernel density estimation of the mean F0 and energy values of speech, synthesized with texts in five different emotions. The blue color ("Ground Truth") denotes the distributions of the ground truth samples in the test set. StyleTTS 2 shows distinct distributions for different emotions and produces samples that cover the entire range of the ground truth distributions.
as displayed in Figure 3 (b). JETS, shown in Figure 3 (c), fails to span the full distribution of F0 and energy, producing samples clustered around the mode. In contrast, our model extends across both F0 and energy distributions, approximating the range of the ground truth.
Since both StyleTTS 2 and VITS are probabilistic while JETS is deterministic, this shows that probabilistic models generate more expressive speech compared to deterministic ones. Moreover, our model demonstrates slightly better mode coverage compared to VITS at the right tail of the energy mean distribution, likely because we use diffusion-based models as opposed to the variational autoencoder used in VITS.
For audio samples of the synthesized speech and their corresponding emotional text, please refer to our demonstration page at https://styletts2.github.io/#emo.
The ablation study results, presented in Table 6, illustrate the importance of each component in our proposed model using several measures: mel-cepstral distortion (MCD), MCD weighted by Speech Length (SL) that evaluates both the length and the quality of alignment between two speeches (MCD-SL), root mean square error of log F0 pitch ( $F_0$  RMSE), mean absolute deviation of phoneme duration (DUR MAD), and word error rate (WER), following [36]. The MCD and  $F_0$  RMSE were computed between the samples generated by each model and the ground truth aligned with dynamic time warping. The MCD-SL was computed using a publicly available library<sup>3</sup>. The WER was computed with a pre-trained ASR model<sup>4</sup> from ESPNet [63].
Most notably, using randomly encoded style vectors instead of those sampled through style diffusion considerably affects all metrics, including the subjective CMOS score, thereby underscoring its
3https://github.com/chenqi008/pymcd
Available at https://zenodo.org/record/4541452
Table 6: Comparison of models in ablation study with mel-cepstral distortion (MCD), MCD weighted by Speech Length (MCD-SL), root mean square error of log F0 pitch ( $F_0$  RMSE), mean absolute deviation of phoneme duration (DUR MAD), and word error rate (WER) on LJSpeech. The CMOS results are copied directly from Table 5 as a reference.
Model	MCD	MCD-SL	F0 RMSE	DUR MAD	WER	CMOS
Proposed model	4.93	5.34	0.651	0.521	6.50%	0
w/o style diffusion	8.30	9.33	0.899	0.634	8.77%	-0.46
w/o SLM adversarial training	4.95	5.40	0.692	0.513	6.52%	-0.32
w/o prosodic style encoder	5.04	5.42	0.663	0.543	6.92%	-0.35
w/o differentiable upsampler	4.94	5.34	0.880	0.525	6.54%	-0.21
w/o OOD texts	4.93	5.45	0.690	0.516	6.58%	-0.15
significance. Since [6] establishes that the style vectors crucially influence all aspects of the speech, such as pauses, emotions, speaking rates, and sound quality, style diffusion is the most important factor in producing speech close to the ground truth and natural human speech.
Excluding adversarial training with large speech language models (SLMs) results in a slight decline in MCD-SL and  $F_{0}$  RMSE but does not impact WER. Interestingly, this version produces the lowest duration error, implying that SLM discriminators may cause minor underfitting on in-distribution texts, which can also be observed in models trained without OOD texts during SLM adversarial training. Nonetheless, subjective evaluations reveal a significant difference for out-of-distribution (OOD) texts for models trained without SLM discriminators (see Appendix D for more discussions).
Training without the differentiable upsampler increases the  $F_{0}$  RMSE, but leaves the MCD, MCD-SL, DUR MAD, and WER unaffected. The removal of the prosodic style encoder affects all metric scores, thus underscoring its effectiveness in our model. Lastly, training without OOD texts only affects the  $F_{0}$  RMSE in our objective evaluation.
Further details, discussions, and audio samples regarding these model variations can be found in Section 8 of our demo page: https://styletts2.github.io/#ab.
Appendix B Style Diffusion
B.1 EDM Formulation
EDM [50] formulates the sampling procedure of diffusion-based generative model as an ordinary differential equation (ODE) (Eq. 1 in [50]):
$$
d \boldsymbol {x} = - \dot {\sigma} (t) \sigma (t) \nabla_ {\boldsymbol {x}} \log p (\boldsymbol {x}; \sigma (t)) d t, \tag {12}
$$
where the noise level schedule and its derivative are represented by  $\sigma(t)$  and  $\dot{\sigma}(t)$ , respectively. The score function of  $p(\boldsymbol{x})$  is  $\nabla_{\boldsymbol{x}} \log p(\boldsymbol{x}; \sigma(t))$ , and  $p(\boldsymbol{x})$  is the targeted data distribution (please refer to Appendix B in [50] for a detailed derivation). The equation can be rewritten in terms of  $d\sigma$  as:
$$
d \boldsymbol {x} = - \sigma \nabla_ {\boldsymbol {x}} \log p (\boldsymbol {x}; \sigma) d \sigma . \tag {13}
$$
The denoising score matching objective is defined as follows (Eq. 108 in [50]):
$$
\mathbb {E} _ {\boldsymbol {y} \sim p _ {\text {d a t a}}, \boldsymbol {\xi} \sim \mathcal {N} (0, \sigma^ {2} I), \ln \sigma \sim \mathcal {N} \left(P _ {\text {m e a n}}, P _ {\text {s t d}} ^ {2}\right)} \left[ \lambda (\sigma) \| D (\boldsymbol {y} + \boldsymbol {\xi}; \sigma) - \boldsymbol {y} \| _ {2} ^ {2} \right]. \tag {14}
$$
Here,  $\pmb{y}$  denotes the training sample from data distribution  $p_{\mathrm{data}}$ ,  $\pmb{\xi}$  is the noise, and  $D$  is the denoiser, defined as (Eq. 7 in [50]):
$$
D (\boldsymbol {x}; \sigma) := c _ {\text {s k i p}} (\sigma) \boldsymbol {x} + c _ {\text {o u t}} (\sigma) F _ {\theta} \left(c _ {\text {i n}} (\sigma) \boldsymbol {x}; c _ {\text {n o i s e}} (\sigma)\right). \tag {15}
$$
In the above equation,  $F_{\theta}$  represents the trainable neural network,  $c_{\mathrm{skip}}(\sigma)$  modulates the skip connection,  $c_{\mathrm{in}}(\sigma)$  and  $c_{\mathrm{out}}(\sigma)$  regulate input and output magnitudes respectively, and  $c_{\mathrm{noise}}(\sigma)$  maps
noise level  $\sigma$  into a conditioning input for  $F_{\theta}$ . With this formulation, the score function is given by (Eq. 3 in [50]):
$$
\nabla_ {\boldsymbol {x}} \log p (\boldsymbol {x}; \sigma (t)) = \left(D (\boldsymbol {x}; \sigma) - \boldsymbol {x}\right) / \sigma^ {2}. \tag {16}
$$
In Table 1 of [50], the authors define the scaling factors as follows:
$$
c _ {\text {s k i p}} (\sigma) := \sigma_ {\text {d a t a}} ^ {2} / \left(\sigma^ {2} + \sigma_ {\text {d a t a}} ^ {2}\right) = \left(\sigma_ {\text {d a t a}} / \sigma^ {*}\right) ^ {2}, \tag {17}
$$
$$
c _ {\text {o u t}} (\sigma) := \sigma \cdot \sigma_ {\text {d a t a}} / \sqrt {\sigma_ {\text {d a t a}} ^ {2} + \sigma^ {2}} = \sigma \cdot \sigma_ {\text {d a t a}} / \sigma^ {*}, \tag {18}
$$
$$
c _ {\text {i n}} (\sigma) := 1 / \sqrt {\sigma_ {\text {d a t a}} ^ {2} + \sigma^ {2}} = 1 / \sigma^ {*}, \tag {19}
$$
$$
c _ {\text {n o i s e}} (\sigma) := \frac {1}{4} \ln (\sigma), \tag {20}
$$
where  $\sigma^{*} := \sqrt{\sigma_{\mathrm{data}}^{2} + \sigma^{2}}$ , a shorthand notation for simplicity.
Inserting equations 17, 18, 19 and 20 into equation 15 yields:
$$
\begin{array}{l} D (\boldsymbol {x}; \sigma) = c _ {\text {s k i p}} (\sigma) \boldsymbol {x} + c _ {\text {o u t}} (\sigma) F _ {\theta} \left(c _ {\text {i n}} (\sigma) \boldsymbol {x}; c _ {\text {n o i s e}} (\sigma)\right) \\ = \left(\frac {\sigma_ {\text {d a t a}}}{\sigma^ {*}}\right) ^ {2} \boldsymbol {x} + \frac {\sigma \cdot \sigma_ {\text {d a t a}}}{\sigma^ {*}} \cdot F _ {\theta} \left(\frac {\boldsymbol {x}}{\sigma^ {*}}; \frac {1}{4} \ln \sigma\right), \tag {21} \\ \end{array}
$$
Combining equation 13 and 16, we obtain
$$
\begin{array}{l} \frac {d \boldsymbol {x}}{d \sigma} = - \sigma \nabla_ {\boldsymbol {x}} \log p (\boldsymbol {x}; \sigma (t)) \\ = - \sigma (D (\boldsymbol {x}; \sigma) - \boldsymbol {x}) / \sigma^ {2} \tag {22} \\ = \frac {\boldsymbol {x} - D (\boldsymbol {x} ; \sigma)}{\sigma}. \\ \end{array}
$$
Equations 2 and 4 can be recovered by replacing  $\pmb{x}$  with  $s$ ,  $p(\pmb{x})$  with  $p(\pmb{s}|\pmb{t})$ ,  $F_{\theta}$  with  $V$  and  $D$  with  $K$  in equations 21 and 22 respectively.
In EDM, the time step  $\{\sigma_0,\sigma_1,\dots ,\sigma_{N - 1}\}$  for the total step  $N$  is defined as (Eq. 5 in [50]):
$$
\sigma_ {i <   N} := \left(\sigma_ {\max } ^ {\frac {1}{\rho}} + \frac {i}{N - 1} \left(\sigma_ {\min } ^ {\frac {1}{\rho}} - \sigma_ {\max } ^ {\frac {1}{\rho}}\right)\right) ^ {\rho}, \tag {23}
$$
where  $\sigma_{\mathrm{max}} = \sigma_0$ ,  $\sigma_{\mathrm{min}} = \sigma_{N - 1}$ , and  $\rho$  is the factor that shortens the step lengths near  $\sigma_{\mathrm{min}}$  at the expense of longer steps near  $\sigma_{\mathrm{max}}$ . Optimal performance is observed in [50] when  $\sigma_{\mathrm{max}} \gg \sigma_{\mathrm{data}}$  and  $\rho \in [5,10]$ . Through empirical tests, we set  $\sigma_{\mathrm{min}} = 0.0001$ ,  $\sigma_{\mathrm{max}} = 3$  and  $\rho = 9$  in our work, allowing fast sampling with small step sizes while producing high-quality speech samples.
B.2 Effects of Diffusion Steps
Table 7: Comparison of mel-cepstral distortion (MCD), MCD weighted by Speech Length (MCD-SL), root mean square error of log F0 pitch ( $F_0$  RMSE), word error rate (WER), real-time factor (RTF), coefficient of variation of duration ( $\mathrm{CV}_{\mathrm{dur}}$ ), and coefficient of variation of pitch ( $\mathrm{CV}_{\mathrm{f0}}$ ) between different diffusion steps.
Step	MCD ↓	MCD-SL ↓	F0 RMSE ↓	WER ↓	RTF ↓	CVdur ↑	CVf0 ↑
4	4.90	5.34	0.650	6.72%	0.0179	0.0207	0.5473
8	4.93	5.33	0.674	6.53%	0.0202	0.0466	0.7073
16	4.92	5.34	0.665	6.44%	0.0252	0.0505	0.7244
32	4.92	5.32	0.663	6.56%	0.0355	0.0463	0.7345
64	4.91	5.34	0.654	6.67%	0.0557	0.0447	0.7245
128	4.92	5.33	0.656	6.73%	0.0963	0.0447	0.7256
The impact of diffusion steps on sample quality, speed, and diversity was studied using multiple indicators. We assessed the sample quality using mel-cepstral distortion (MCD), MCD weighted by
Speech Length (MCD-SL), root mean square error of log F0 pitch ( $F_0$  RMSE), and word error rate (WER). We also examined the influence of diffusion steps on computational speed using the real-time factor (RTF), which was computed on a single Nvidia RTX 2080 Ti GPU. Given the application of the ancestral solver, which introduces noise at each integration step [52], it was postulated that a higher number of steps would lead to more diverse samples. This hypothesis was tested using the coefficient of variation on the F0 curve ( $\mathrm{CV}_{\mathrm{f0}}$ ) and duration ( $\mathrm{CV}_{\mathrm{dur}}$ ).
The model was run with diffusion steps ranging from 4 to 128, as results with only two steps were disregarded due to the characteristics of the noise scheduler in EDM. Here,  $\sigma_0 = \sigma_{\mathrm{max}}$  and  $\sigma_{N - 1} = \sigma_{\mathrm{min}}$ , and therefore, with merely two diffusion steps, the attainment of samples of acceptable quality is not feasible.
The results presented in Table 7 reveal negligible disparities in sample quality across varying diffusion steps, with satisfactory quality samples being producible with as few as three steps in our experiments. Consequently, we decided to randomly sample styles with diffusion steps ranging from 3 to 5 during training in order to save time and GPU RAM. Furthermore, we observed an incremental rise in speech diversity relative to the number of diffusion steps, reaching a plateau around 16 steps. Thereafter, the increase is marginal, with a slight decrease in diversity noticed when the steps are large, potentially attributable to the refining of the noise schedule with higher step counts. This could cause the ancestral solver to converge to a fixed set of solutions despite the noise introduced at each step.
Notably, optimal results in terms of sample quality and diversity, paired with acceptable computational speed, were achieved with around 16 diffusion steps. Even though the RTF experienced a  $30\%$  increase compared to 4 steps, it still outperformed VITS by twice the speed, making it a suitable choice for real-time applications.
B.3 Consistent Long-Form Generation
Algorithm 1 Long-form generation with style diffusion  
procedure LONGFORM(t,  $\alpha$ ) ▷ t is the input paragraph,  $\alpha \in [0,1]$  is the weighting factor  
T ← SPLIT(t) ▷ Split t into N sentences {T0,T1,...,TN-1}  
for i ∈ {0,...,N-1} do  
scurr ← STYLEDIFFUSION(Ti) ▷ Sample a style vector with the current text Ti  
if sprev then  
scurr ← αscurr + (1 -  $\alpha$ )sprev ▷ Convex combination interpolation  
end if  
xi ← SYNTHESIZE(Ti, scurr) ▷ Synthesize with the interpolated scurr  
sprev ← scurr ▷ Set sprev for the next iteration  
end for  
return CONCAT({x0,x1,...,xn-1}) ▷ Return concatenated speech from all sentences  
end procedure
Our findings indicate that style diffusion creates significant variation in samples, a characteristic that poses challenges for long-form synthesis. In this scenario, a long paragraph is usually divided into smaller sentences for generation, sentence by sentence, in the same way as real-time applications. Using an independent style for each sentence may generate speech that appears inconsistent due to differences in speaking styles. Conversely, maintaining the same style from the first sentence throughout the entire paragraph results in monotonic, unnatural, and robotic-sounding speech.
We empirically observe that the latent space underlying the style vectors generally forms a convex space. Consequently, a convex combination of two style vectors yields another style vector, with the speaking style somewhere between the original two. This allows us to condition the style of the current sentence on the previous sentence through a simple convex combination. The pseudocode of this algorithm, which uses interpolated style vectors, is provided in Algorithm 1.
An example of a long paragraph generated using this method with  $\alpha = 0.7$  is available on our demo page: https://styletts2.github.io/#long.
Figure 4: Illustration of our proposed differentiable duration upsampler. (a) Probability output from the duration predictor for 5 input tokens with  $L = 5$ . (b) Gaussian filter  $\mathcal{N}_{\ell_{i-1}}$  centered at  $\ell_{i-1}$ . (c) Unnormalized predicted alignment  $\tilde{f}_{a_i}[n]$  from the convolution operation between (a) and (b). (d) Normalized predicted alignment  $a_{\mathrm{pred}}$  over the phoneme axis.
B.4 Style Transfer
Our model, contrary to traditional end-to-end (E2E) TTS systems that directly map texts to speech-related representations, decouples speech content and style via a style vector. This setup enables the transfer of a style sourced from a text with specific emotions or stylistic nuances to any given input text. The procedure involves generating a style vector from a text reflecting desired aspects of the target style (e.g., emotions, speaking rates, recording environments, etc.) and synthesizing speech from any input text using this style vector. This process also extends to unseen speakers, as demonstrated on our demo page at https://styles2.github.io/#libri. It is important to note that the relationship between speech style and input text is learned in a self-supervised manner without manual labeling, analogous to the CLIP embedding used in Stable Diffusion.
Appendix C Differentiable Duration Modeling
C.1 DSP-Based Formulation
In section 3.2.4, we formulated the differentiable duration model using probability theory. This section provides an alternative formulation based on principles of digital signal processing (DSP). The upsampler is formulated using the following DSP properties:
$$
x [ n ] * \delta_ {k} [ n ] = x [ n + k ], \quad \tag {25}
$$
Here,  $\delta_{k}[n]$  is the Kronecker delta function,  $\delta_c(x)$  is the Dirac delta function, and  $\mathcal{N}_c(x;\sigma)$  is the Gaussian kernel centered at  $c$ , as defined in eq. 7. These properties state that a delta function,  $\delta_{k}$ , through convolution operation, shifts the value of a function to a new position by  $k$ , and a properly normalized Gaussian kernel converges to the delta function as  $\sigma \to 0$ . Therefore, by properly selecting  $\sigma$ , we can approximate the shifting property of the delta function with  $\mathcal{N}_c(x;\sigma)$  and adjust the duration predictor output  $q[:i]$  to the starting position  $\ell_{i-1}$  of the  $i^{\text{th}}$  phoneme. Figure 4 illustrates our proposed differentiable duration modeling, and a real example of the duration predictor output, the predicted alignment using the non-differentiable upsampling method described in section 3.1, and the alignment using our proposed method is given in Figure 5.
(a) Predictor output.
(b) Non-differentiable upsampling.
Figure 5: An example of duration predictor output and the predicted alignment with and without differentiable duration upsampler. (a) displays the log probability from the duration predictor for improved visualization. Although (b) and (c) differs, the duration predictor is trained end-to-end with the SLM discriminator, making the difference perceptually indistinguishable in synthesized speech.
(c) Proposed upsampling with  $\sigma = 1.5$
C.2 Effects of  $\sigma$  on Training Stability and Sample Quality
The gradient propagation from the SLM discriminator to the duration predictor is crucial for our model's performance. The norm of this gradient, computed using the chain rule, is the product of the norms of individual functions' gradients. The magnitude of the partial derivatives of  $\mathcal{N}_c(x;\sigma)$  is:
$$
\left| \frac {\partial \mathcal {N} _ {c} (x ; \sigma)}{\partial x} \right| = \left| \frac {\partial \mathcal {N} _ {c} (x ; \sigma)}{\partial c} \right| = \left| \pm \frac {(x - c)}{\sigma^ {2}} \mathcal {N} _ {c} (x; \sigma) \right| = \frac {| x - c |}{\sigma^ {2}} | \mathcal {N} _ {c} (x; \sigma) | \leq \frac {C}{\sigma^ {2}}, \tag {26}
$$
where  $C$  is a constant dependent on  $L$ . The gradient norm is thus of order  $O(1 / \sigma^k)$ , where  $k$  depends on the input dimension. As  $\sigma \to 0$ ,  $\mathcal{N}_c(x;\sigma)$  approaches  $\delta_c(x)$ , but the gradient norm  $\| \nabla \mathcal{N}_c(x;\sigma)\|$  goes to infinity. Moreover, smaller  $\sigma$  values may not yield better  $\delta$  approximations due to numerical instability, particularly when dealing with small values such as the predictor output  $q\in [0,1]$ , which is multiplied by  $\mathcal{N}_c(x;\sigma)$  in eq. 11, exacerbating numerical instability.
We investigate the trade-off by examining the impact of  $\sigma$  on training stability and sample quality. Training stability is represented by the gradient norm from the SLM discriminator to the duration predictor, and sample quality is measured by the mel cepstral distortion (MCD) between speech synthesized using the predicted alignment generated through the non-differentiable upsampling method (section 3.1) and our proposed differentiable upsampling method. We use the maximum gradient over an epoch to represent training stability, as large gradient batches often cause gradient explosion and unstable training. We compute these statistics for  $\sigma$  values from 0.01 to 10.0 on a log scale, with results shown in Figure 6.
(a) Sample quality (MCD)
(b) Training stability (max gradient norm)
Figure 6: Effects of  $\sigma$  on MCD and max gradient norm. Our choice of  $\sigma = 1.5$  is marked with a star symbol. (a) MCD between samples synthesized with differentiable and non-differentiable upsampling over different  $\sigma$ . (b) The maximum norm of gradients from the SLM discriminator to the duration predictor over an epoch of training with different  $\sigma$ .
Our chosen  $\sigma = 1.5$  minimizes both MCD and gradient norm across a wide range of  $\sigma$  values, making it an optimal choice for upsampling. This value also aligns with the typical phoneme duration of 2 to 3 speech frames, as Gaussian kernels with  $\sigma = 1.5$  span approximately 3 speech frames.
Appendix D SLM Discriminators
D.1 Layer-Wise Analysis
Following the approach of the WavLM study [11], we performed a layer-wise feature importance analysis for the speech language model (SLM) discriminator. The weights of the input linear projection layer into the convolutional discriminative head were normalized using the average norm values of WavLM features for samples in the test set across LJSpeech, VCTK, and LibriTTS datasets. Figure 7 depicts the normalized weight magnitudes across layers.
In the LJSpeech and LibriTTS models, the initial (1 and 2) and middle layers (6 and 7) showed the highest importance, while the final layers (10 and 11) were of diminishing importance. The least influential was the final layer, consistent with previous findings that ascribe the primary role of this layer to pretext tasks over downstream tasks [55, 10, 11]. Notably, layers 0, 1, and 2, which encode acoustic information like energy, pitch, and signal-to-noise ratio (SNR), emerged as the most
(a) LJSpeech
(b) VCTK