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Highlights of SSW and INTERSPEECH 2021

We were so blessed to be able to attend INTERSPEECH in person this year! (Seriously - Czechia changed their covid laws literally the week before the conference began, making it possible for us to go last minute.) Unfortunately we were not able to make it to Budapest to attend Speech Synthesis Workshop (SSW) in person, but the hybrid setup of both conferences really made for a wonderfully accessible experience for both in-person and remote attendees. Similar to last year, all papers were accompanied by a 3 minute video, which allowed attendees to get an aural overview of each work in addition to abstracts. Even if conferences go fully in-person in the future, we hope this video accompaniment will stick around!

As you may recall from our highlights post last year, we are particularly interested in speech synthesis topics, including multilingual TTS; generation, transfer, and control of expressive speech (including prosody and emotion); vocoder research; and more. Ultimately, within our realm of video translation, we hope to both generate the full range of audible human expression within our systems, and expand our language coverage.

Also don’t miss our own papers at INTERSPEECH this year, Mohan et al. 2021 and Torresquintero et al. 2021, for which we wrote two blog posts Ctrl-P and ADEPT respectively.

Without further ado, here are some of the highlights we noticed this year. You can skip to a specific section here:


There’s always cause to be excited about new training data! For English, we noticed the Hi-Fi dataset, and RyanSpeech. The Hi-Fi corpus includes at least 15 hours of data for each of its 10 speakers, which is a huge improvement over previous mainstream multispeaker corpora (LibriTTS averages ~4 hours per speaker, and VCTK averages less than 30 minutes per speaker). Excitingly, it’s available for commercial use. RyanSpeech is a single speaker male corpus that aims to increase the availability of male speech data. Considering that LJSpeech and the 2013 Blizzard corpus, which are both female speakers, are two of the most-used single speaker English corpora, this is a great new resource to the community.

For Mandarin, EMOVIE and AISHELL-3 caught our attention. The EMOVIE data comes from movies, and thus is the first public Mandarin TTS corpus designed specifically for emotional synthesis, though it’s only available for research. And AISHELL-3 is a large multi-speaker corpus (85 hours of 218 speakers) that aims to allow the community to expand beyond English in mainstream TTS research. Given that the corpus is publicly available, it would be great to see more non-English research!

And last but not least, we were excited to see KazakhTTS. Though we at Papercup probably won’t have a chance to work with Kazakh in the near future, it’s wonderful to see more open-source data for low-resource languages.

Linguistic Frontend

On the linguistic frontend side, polyphone disambiguation was a hot topic this year. Theoretically, this is the same problem as homograph disambiguation, but the literature tends to use polyphone disambiguation specifically in the context of Chinese characters, and the approaches tend to be a bit different. Last year we were particularly intrigued by g2pM, a publicly released python library for Mandarin that converts Chinese characters into pinyin, with a focus on polyphones. If you’re interested in Mandarin TTS and haven’t looked at this library yet, seriously check it out. This year, Zhang, Shi et al., Li et al., and Choi et al. all contributed further to polyphone disambiguation research. Though the first three show accuracy improvements against g2pM, as far as we can tell none of the papers this year released an easy-to-use python package. So for now we’re still keen on g2pM as a ready-to-use polyphone disambiguation system.

We also saw some work on prosodic boundary prediction. Futamata et al. show that implicit features on tokens from BERT, and explicit features (like POS tags and dependency parses) from a BiLSTM improves prediction, which is particularly important for Japanese as a pitch-accent language. Trang et al. similarly use unnamed constituency trees (without phrase labels like NP or VP) and POS tags for prosodic boundary prediction in Vietnamese. Though we generally prefer dependency parses to constituency parses because they are more generalisable to languages with free word order, it’s still cool to see more applications of constituency trees for non-European languages. Lastly, Zou et al. explore using ToBI labels to annotate prosody explicitly. Though their samples sound pretty expressive and correspond to what you would expect from a particular ToBI label, they trained their ToBI predictor on data hand-labeled by a linguist. We’re keen to see if other automated ways of obtaining ToBI or other labels can help with research in this area.


As there has been more and more research on expressive TTS, so too has there been more research into evaluating it. Baird et al. introduce a prototypical network to evaluate similarity, diversity, and plausibility of generated emotional samples; Rallabandi et al. share objective metrics for perception of warmth, competence, and extraversion; and Gutierrez et al. demonstrate the benefits of “expectation-driven” prosodic evaluation based on question-answer pairs. Side note: we also used question-answer pairs to evaluate topical emphasis in ADEPT!

We also want to give a shoutout to O’Mahony et al., who investigate what factors lead to different MOS scores. In particular, they find that whether or not there is provided context, ratings for naturalness are mostly the same. But when rating appropriateness, synthesised speech gets a higher MOS rating when given in context, than when given in isolation.

Lastly, there were several papers that investigate objective/automated metrics, including Tseng et al., Kongthaworn et al., Taylor et al., and Valentini-Botinhao et al..


Tacotron 2 has reigned for a few years now as the default workhorse (phone-to-mel-spectrogram) acoustic model for TTS research. This is gradually shifting towards non-autoregressive (NAR) Transformer-based models, in particular, we noticed a significant uptake of FastSpeech 2 as the base model for TTS research this year.

Parallel Tacotron 2 (samples) by Elias et al. attempts to improve NAR TTS by proposing a fully differentiable phone duration model that learns the token-frame alignments without needing supervised duration signals or an attention mechanism. They achieve this by translating predicted phone durations into Token Boundary Grids that represent the distance from start and end of each token interval, thus transforming a hard duration prediction into a differentiable one. To deal with overall duration mismatch, they use soft dynamic time warping to compute the loss.

Greater controllability and expressiveness

Similar to last year, a large number of TTS papers continue to focus on expressivity and prosody modelling, but controllability and interpretability have become a central consideration. Along with the explicit duration modelling in NAR models, this has revived the use of explicit acoustic features - most commonly F0 and energy (as well as duration if not a NAR model). During training, these acoustic features are extracted from the reference mel-spectrogram at the frame level, or at the phone level using an external aligner, most commonly the Montreal Forced Aligner.

During inference, these acoustic feature values are either obtained from some reference audio (e.g. Lee et al.) or predicted from text, given other conditioning information such as speaker or style (e.g. Pan et al.). Optional control is available either directly by modifying the acoustic features (e.g. our paper Ctrl-P) or indirectly by manipulating higher-level input features to the prosody prediction network, including style tags (Kim et al.), arousal-valence scalars (Sivaprasad et al.), and ToBI labels (Zou et al.). With these indirect control levers, the prosody prediction network often required manual annotations of the higher-level input features during training. This is an area of opportunity - finding the right higher-level abstraction of prosody that is simultaneously interpretable, intuitive, rich in expression, and possible to annotate automatically, could lead to a step-change improvement in controllable expressive TTS.

To this end, we really liked the idea in Kim et al. (samples) of representing style using natural language style tags that are embedded with Sentence-BERT (SBERT). The open vocabulary (whatever was seen in SBERT’s training) offers a rich yet intuitive way to i) elicit nuanced emotions or attitudes during data collection ii) represent these to the model by harnessing large pretrained language models, and iii) specify the desired affect during inference. It’s an enticing notion - that the learnt representations of the range of emotions and attitudes in natural language might align with the spectrum of acoustic characteristics in the corresponding speech.

Sivaprasad et al. also demonstrated a promising approach by training their prosody control block on non TTS-grade data that had valence-arousal labels, independently from the encoder-decoder TTS module which was trained on TTS-grade data. Decoupling the training of the prosody prediction network from the core (encoder-decoder) TTS synthesiser opens up myriad possibilities as TTS-grade data is expensive and usually limited in variation.

Beyond explicit acoustic features, there were a host of papers that continued to pursue learnt approaches to model prosodic variation. Many of these papers extended Tacotron with Reference Encoder or Global Style Tokens (GST) to incorporate a temporal latent, and focussed on disentanglement for the purposes of style transfer and latent space interpretability. Li et al. proposed a multi-scale reference encoder that extracts both global-scale (utterance-level) and local-scale (quasi-phoneme-level through downsampling) features. The latter is aligned to the phone sequence using a reference attention. Tan et al. disentangles content information from the learnt style embedding space by adding a separate content encoder. Both content and style encoders received additional collaborative and adversarial training supervision, respectively, from phones obtained through forced alignment. Tjandra et al. goes a step further to learn disentangled content and style latent spaces without any supervision. Instead of collaborative and adversarial training, they use mutual information minimisation and employ a VQ-VAE to model the temporal latent as inductive bias for the content encoder.

Within expressivity, there were also a number of papers that focussed on conversational and spontaneous speech. Cong et al. (samples) model spontaneous behaviour in speech - prolongation and filled-pauses (manually annotated) - as attributes of phones. They also model sentence entrainment by providing the model with a BERT encoding of the current sentence and an acoustic encoding of the previous sentence. Going beyond the magic number 2 for TTS models, Yan et al. released AdaSpeech 3 (samples) to address model adaptation for spontaneous-style speech with limited data. They model filled-pause (ASR transcribed) with a predictor that inserts these into the text sequence, train a mixture-of-experts duration model to account for varying rhythm, and propose a parameter-efficient model adaptation routine.

There were three other papers related to expressivity and controllability that caught our attention. Jia et al. (samples) proposed a new TTS text encoder called PnG BERT. This is an augmented BERT model that takes both phoneme and grapheme representations of text as input. The paper showed that pre-training the model on a large text corpus in a self-supervised manner, followed by fine-tuning on the TTS task leads to more natural prosody and accurate pronunciation. We would be interested to see how this approach compares against a TTS-model conditioned on BERT embeddings.

Bae et al. (samples) proposed to improve Transformer-based NAR TTS models by incorporating more hierarchical structure, constraining the attention range of the encoder and decoder to induce each layer to focus on the appropriate contextual information. The text encoder is first constrained to have a narrow attention window, then gradually widened as the layers progress (local to global), while the audio decoder has the reverse wide to narrow structure (global to local). Hierarchical pitch embeddings are also provided explicitly at both sentence and word levels.

Rijn et al. (samples) applied the concept of Gibbs Sampling to design an annotation process that discovers prototypes of emotional prosody from the learnt latent space of a trained GST model.

Cross-lingual and speaker-adaptive synthesis

We were happy to see a number of papers (Wells and Richmond, Fong et al., Maniati et al.) continue along the lines of our paper (blogpost) from Interspeech last year, using phonological features as a basis for cross-lingual synthesis research. Maniati et al. (samples) conducted a comprehensive study of the effect of phonological similarity across different source-target language combinations, the effect of having the adaptation language in training, and the quantity of adaptation data on 0-shot cross-lingual TTS performance.

Most papers focussed on the low-resourced aspects of multilingual synthesis, but few directly addressed the native accent issue in cross-lingual synthesis, whereby the target speaker’s native accent leaks into the synthesised speech of a different language. Shang et al. (samples) attempted to mitigate this by removing speaker information from the style and text encoder with an adversarial speaker classifier. While this reduced the accent leakage, there remains a gap in naturalness score between cross-lingual synthesis and native-language synthesis. We think this is a research area with large potential as solving this issue would massively boost the scalability of TTS across languages.


As in previous years, this year’s crop of research around waveform generation strived towards vocoders that were faster, more compute and data efficient, higher quality, and more universal than their predecessors. However, architecturally, the field seemed to be converging on a single class of ML models: GANs. Why were GANs becoming so popular? A number of papers cited the benefits of faster training and inference speeds while maintaining quality on par with or better than existing state-of-the-art autoregressive and flow-based vocoders. However, we clearly weren’t the only ones taking notice of their recent success. In particular, You et al. hypothesized that the reason behind the success of the latest generation of GAN vocoders is the multi-resolution discriminating framework. They paired 6 different GAN-based vocoders with the multi-resolution discriminator from Hifi-GAN and found no significant difference in terms of MOS or MCD, suggesting that as long as the generators are reasonably good (that is, having sufficient capacity to model long term dependencies in the audio), the choice of generator architecture is not as important as the discriminator.

While GANs have been undeniably successful and widely adopted, Perrotin et al. rightly points out that neural vocoders are often evaluated in their “comfort zones”, e.g. on test data that closely resembles the training data. This says nothing about a given vocoder’s ability to generalise to unseen data, like particularly expressive speech with a wider F0 range or languages with different phone sets. When evaluated on unseen data in a head-to-head comparison against other neural vocoder architectures, do GANs live up to the current hype? They carefully control the range of seen F0 values in the training data and assessed the quality of synthesized audio both globally (RMSE) and locally (RMSE outliers and frame-based F0 errors) on a test set of extreme F0 values. Their results showed that: autoregressive WaveRNN, hybrid signal-neural LPCNet, and GAN-based Parallel WaveGAN all experienced different failure modes. The exception being the flow-based vocoder WaveGlow which seemed relatively robust to unseen F0. Unfortunately for those of us interested in vocoding more expressive utterances, this suggests that each vocoder might require different adaptation strategies in order to generalise well.

End-to-end TTS

This year, we saw comparatively fewer papers exploring the realm of end-to-end (E2E) TTS (by which we mean models that take as input graphemes/phonemes and output raw audio). While we’re not sure if this is any indication of the maturity of this research, it appears that E2E approaches may still be grappling with alignment stability between the input text and output audio. Chung et al. were motivated to tackle this using a reinforcement learning setup to learn a robust internal aligner, while Gong et al. made a case for conditioning LPCNet on mel-spectrograms instead of explicit pitch parameters and Bark-scale cepstral coefficients (BFCCs) to mitigate alignment errors from the Tacotron2-based acoustic model on longer utterances. On the other hand, several of the latest E2E approaches cited quality on-par with non-E2E baselines in addition to simplified training and lighter models. Chen et al. and Nguyen et al. both do this by removing the intermediate mel-spectrogram decoding step and swapping in an adapted vocoder as a waveform decoder, in these cases WaveGrad and Hifi-GAN respectively. Given the quality of some of these samples, we’ll definitely be keeping an eye out for the wider adoption of these E2E approaches.

Honourable mentions

Despite our best efforts to span the ever-expanding realm of speech, we tend to focus overwhelmingly on the areas in which we most actively do research. Luckily, conferences like Interspeech and SSW expose us to a myriad of research threads we might otherwise have completely overlooked.

For example, why limit ourselves to speech synthesis? If text is to speech as notes are to music, surely music synthesis is a natural evolution of the field! Obviously Cooper et al. thought so when they adapted existing TTS and waveform systems like Tacotron2 and the neural source-filter (NSF) model respectively to the synthesis of piano from MIDI input. While it seems like physical models still outperform the proposed deep learning approach, the initial quality speaks to the general flexibility and adaptability of these frameworks to sound synthesis tasks.

We’re also learning new things about speech all the time. Case in point: the Lombard effect, which apparently refers to the involuntary tendency to speak louder in the presence of noise, causing changes in F0, energy, and speech rate. Since we can’t expect to be using TTS only in perfectly quiet environments, it seems obvious that our systems should adapt to their surroundings. That’s exactly what Novitasari et al. try to do by building a machine speech chain to establish a feedback loop between listening (ASR) and speaking (TTS) components. By incorporating information about mismatches in the ASR transcribed text and the synthesised text, the TTS system is able to get per utterance feedback and dynamically adapt the subsequent speech prosody — a neat idea even outside of the Lombard context!

Another aspect that has been interesting to observe is the growing interest in the utility of learned speech representations in downstream TTS tasks. Typically evaluated in the context of ASR, this year saw speech representations learned through Self-Supervised Learning (SSL) methods being applied to downstream TTS-oriented tasks like resynthesis and voice conversion (Polyak et al.). Leveraging the way VQ-VAEs cluster the latent space into discrete prototype centroids, Fong et al. and Williams et al. investigate how VQ codes corresponding to phone and speaker representations can be used to manipulate speech, in particular for creating new voices and modifying word pronunciation. While different speech representations may be more or less suitable for different tasks, this certainly appears to be a promising thread for the future of controllable speech synthesis.

And that’s a wrap! Hope to see you at INTERSPEECH 2022 in Incheon, and SSW 2023 in Grenoble!

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Papercup Machine Learning Team

Papercup Machine Learning Team

We build expressive voices for Machine Dubbing in many languages!

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