/14 min read

Highlights of Interspeech 2022

This year Interspeech was hosted in Korea. It was our first time visiting this beautiful country and we can’t wait to go back again! We had the most amazing time in Incheon; the karaoke bars were especially great - I think we went to one almost every night. A big thanks to all those involved in organising the conference and to all those who attended and made it so much fun.

We saw so much great research this year that we’ve decided to write two blog posts. In this first one, we’ll cover our favourite papers and self-supervised learning. In the coming years we think this topic will have a big impact on speech technology.

Our second blog post will cover all things TTS, look out for it next week!

Hot topic: Self-supervised learning applications

Self-supervised learning is the process of training a model to solve a pseudo-task in the hope that the trained model with be useful for a downstream task. At Interspeech, we saw papers that covered a few different aspects of self-supervised learning. The first and most intuitive is the development of pre-trained models and their application for downstream tasks. Second, we saw several papers studying the learnt representations directly, looking specifically at the phonetic information they capture. Finally, others used pre-trained models as feature extractors, using learnt representations to enable novel model architectures.

Pre-training and fine-tuning

Self-supervised learning has made waves in other fields, leading to ground breaking results particularly in natural language generation and image synthesis. We find ourselves in the fine-tuning era, where you can improve performance easily by utilising a pre-trained model. It was only a matter of time before someone published a paper doing exactly that, but for TTS. Kim et al. from Seoul National University pre-trained a VITS model on audio-only data and, after fine-tuning on only 10 minutes of TTS data, achieve an excellent MOS. Their pre-training and fine-tuning data are from the same single-speaker dataset which could make the fine-tuning more likely to succeed on the smallest data size. They also demonstrate good performance on zero-shot TTS, getting a MOS close to a model that has access to the reference speech. In typical fine-tuning approaches the output layer of the pre-trained model is replaced, whereas here the input layers of the model was replaced with a phone encoder so that the final TTS model can be driven by text input.

Speech-to-speech translation is a very challenging problem that has already benefitted from self-supervised learning (see work from Meta’s Universal Speech Translator project). At Interspeech, Popuri et al. from Meta AI extended their previous work using two pre-trained models to initialise their model. The source language speech is encoded using wav2vec 2.0, and the output embeddings are mapped to HuBERT discrete units with a pre-trained text decoder — mBART’s decoder. The pre-trained encoder and decoder are both fine-tuned on the speech-to-unit task. A HiFi-GAN vocoder is trained to vocode the discrete units into a waveform. Their model performs well on synthetic target speech, improving the translation and pronunciation quality. We were excited that the paper included results with real speech as the target, but unfortunately these results were limited to a comparison of BLEU scores with their previous work, it was unclear how well the models trained for real speech performed compared to cascaded systems.

In this model, the two intermediate representations are very different before fine-tuning begins: the pre-trained encoder output is in the source language speech space, while the pre-trained decoder input is in the target language speech space. This makes the question of “where does the translation happen” for this architecture very intriguing. The translation clearly won’t happen in the single convolutional layer that connects the encoder and decoder, suggesting that the fine-tuning fundamentally changes what the pre-trained models are doing.

While there are many models that learn text- or audio- based representations, there are fewer that learn from both modalities together. Prior work at Google achieved this using different objectives for each modality. In MAESTRO, Chen et al. propose two new losses to learn a joint embedding space from both speech and text: the first encourages the embeddings for each modality to be similar, while the second is a language modelling objective in the joint embedding space and works for paired and unpaired data. They achieve SotA performance on both ASR and speech translation. Finally, they show that additional pre-training tasks can further improve downstream performance for speech translation.

Despite the recent shift towards TTS systems that no longer use phonemes as intermediate representations, we still saw the application of pre-trained models to grapheme-to-phoneme (G2P) conversion. As we saw at Interspeech last year, T5G2P (a T5 model fine-tuned on a sentence-level G2P task) significantly outperforms encoder-decoder word-level approaches. Building on this finding, Zhu et al. investigated how two pre-trained variants of T5, the byte-based byT5 and multilingual mT5, perform in a multilingual G2P setting, for which they fine-tuned the models on a collection of open-source pronunciation lexica in no less than 99 languages that they kindly made publicly available!

The authors show that the fine-tuned byT5 model outperforms both its mT5 equivalent and monolingual counterparts, which indicates that using a byte-level encoding and simultaneously training on data from multiple languages improves G2P performance. Interestingly, they also show that additional fine-tuning to individual language data on the multilingual byT5 model further reduces phone error rate on some languages, suggesting that using pre-trained multilingual models can be beneficial in low-resource monolingual G2P.

Analysis of hidden representations

While most papers presented very promising results on a range of speech problems through the application of self-supervised learning models, a few papers instead took a closer look at the information encoded in their representations in order to better understand how they benefit downstream tasks.

In line with the fine-tuning approaches discussed above, tom Dieck et al. adapted a pre-trained wav2vec 2.0 model to perform a phone recognition task and visualised its hidden layer activations to shed light on the level of phonetic detail they contain. Interestingly, 2D PCA on the activations for vowels revealed a striking resemblance to the vowel chart, i.e. the arrangement of vowels in a 2D space based on how high/back the tongue is, indicating sensitivity to F1 and F2, the acoustic correlates of vowel height and backness. A similar analysis on consonants showed that the activations can encode articulatory relationships between phonological pairs (e.g. between plosives and fricatives: /d/ is to /ð/ like /g/ is to /ɣ/), which suggests that the model can also learn information about place and manner of articulation.

Similarly, Wells et al. demonstrated that discrete units derived from HuBERT’s representations encode phonetically interpretable information. A fine-grained analysis of unit-to-phoneme correspondences revealed that units can capture sub-phonetic events, such as the dynamic manner of articulation of plosives (i.e. closure followed by release). However, the degree of articulatory information present in these units seems to vary depending on phoneme category — place of articulation isn’t so distinctively captured in plosives, for example — suggesting different categories have different priorities.

Lastly, de Seyssel et al.’s work on CPC representations presents evidence that self-supervised speech representations can encode language and speaker (by proxy of gender), as well as phonetic, information. In addition to qualitative visualisations such as the above, they obtained quantitative measures from logistic regression classifiers trained on the phone-level CPC representations (one for each information category) and compared the results for monolingual and bilingual models. While gender and phonetic information is present in the output representations of both models, they show that only bilingual models encode disentangled language information.

Even though there’s no question phonetic information is, in some form, present in self-supervised speech representations, it would be interesting to see if they also encode prosodic information, and if so, how it transfers to different downstream speech tasks. Either way, these findings manifest the potential for these representations to be used as input features to a TTS system, which we discuss in the next section.

Use of pre-trained embeddings for TTS

We have found it useful to differentiate between methods that fine-tune self-supervised learning models and approaches that use these pre-trained models as feature extractors.

The low-resource TTS paper from Seoul National University, mentioned above, not only presents a method for pre-training and fine-tuning a TTS model, but also makes use of another pre-trained model’s embeddings to enable pre-training on audio-only data. Kim et al. use a wav2vec 2.0 model to extract “pseudo phonemes”, i.e. discretised units learnt by wav2vec 2.0. These pseudo phonemes are passes as input to the acoustic model during pre-training. This design is what allows pre-training to work with audio-only data. During fine-tuning, they replace this feature extractor with a typical phoneme encoder that takes phonemes extracted from text.

Fong et al.’s Speech Audio Corrector (SAC) presents another interesting use of self-supervised representations. In a similar manner, they discretise HuBERT’s hidden representations into word-aligned “speech codes” which they then use as a substitute for certain words in the otherwise graphemic input to a transformer-based acoustic model.

They show that, by supplying the speech codes for the desired reading of a specific word at inference time, the model is able to successfully correct one-off mispronunciations with maintained synthesis quality, even when said reading is obtained from a non-target speaker with a mismatched accent. SAC thus manages to achieve pronunciation control in a grapheme-based TTS system, a common problem in other end-to-end approaches seeking to remove phonemes from the equation by directly mapping graphemes to speech acoustics. As the authors suggest, this method could be particularly useful in low-resource settings, where pronunciation lexica may not be available.

Finally, in WavThruVec Siuzdak et al. presented a method to reduce the difficulty in training end-to-end models. Instead of beginning from scratch, embeddings from a pre-trained model (they use wav2vec 2.0) serve as an intermediate representation, similar to how mel filter-bank features (aka mel spectrograms) are the typical interface between acoustic modelling and vocoding. While WavThruVec is trained in two stages — text to wav2vec features, and wav2vec features to waveforms — we think this use of a pre-trained intermediate embedding would work well in the joint training paradigm too, like JETS.

Honourable mentions

There were so many papers that we couldn’t make space for in this post or in our overview of TTS at Interspeech (stay tuned for that instalment). But we couldn’t resist sharing a few of our favourites.

An Automatic Soundtracking System for Text-to-Speech Audiobooks

Much of the research in our field focuses on the core technology, such as prosody prediction or vocoding. However, in many cases the most impactful topic for the end-use case lies elsewhere. We thought Bytedance’s research on automatically adding background music to audiobooks was a really impactful contribution to the end-listener experience for audiobooks. Much research has been conducted on automatic synthesis of audiobook data, but Chen et al. are the first to automatically add background music, something that would normally be done manually in post-production.

Their method partitions a chapter into consecutive plot-chunks made up of at least two paragraphs (defined by a set of annotation rules, e.g. a change of time or location). Partitioning is defined as a sequence labelling task where the inputs are BERT embeddings of whole paragraphs. For each plot-chunk, they then perform classification of a plot’s sentiment tag using the paragraph BERT embeddings for that plot-chunk. The sentiment tags include 12 pre-defined categories, including happiness, romance, highlight, and neutral event. Music is then selected heuristically from their catalogue based on plot length and ratio of narration to dialogue.

While manual music selection will likely be the norm for human-produced audiobooks for the foreseeable future, this method will certainly be very impactful for synthetic audiobook production. It also could pave the way to creating new tools to support human-produced audiobooks, just as image synthesis models are doing for art.

SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping

Probabilistic modelling has a long history in TTS. The ability to enforce distributional assumptions that encode expert knowledge about speech has always been a benefit. Recently, flows and diffusion models have demonstrated excellent performance for a variety of speech applications. It is always nice to see the probabilistic modelling framework being taken advantage of by encoding obvious assumptions.

In SpecGrad, by Koizumi et al., the prior from which the diffusion model samples is determined by the input spectrogram. Unlike its predecessor, PriorGrad, this model does not make typical independence assumptions in the prior’s covariance matrix. To make computing a full covariance matrix tractable, SpecGrad reframes the problem as a filter in the time-frequency domain with three components: the STFT, the STFT’s pseudo inverse, and a diagonal matrix of filter coefficients. The model already performs very well, and we’re excited to see additional extensions that incorporate more ideas from signal processing!

JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech

Instead of training two separate stages (acoustic model and vocoder), the Lim et al. have created a setup in which FastSpeech2 and HiFi-GAN can be jointly trained in a single E2E setup. As part of their system, they add an alignment learning framework that encodes the text and mel spectrogram together, rather than relying on an external forced aligner such as MFA. It begs the question, has TTS progressed far enough that we may no longer need to keep these stages separate?

Back to the Future: Extending the Blizzard Challenge 2013

Repeatedly with our own research, we’ve found difficulty in interpreting MOS due to (1) its relative nature, where results are not comparable between different evaluations, and (2) its over-saturation, where models’ scores aren’t statistically significantly different; sometimes human speech also lies within a model’s confidence interval. Therefore, seeing these internal findings validated empirically by Le Maguer et al. was refreshing, because it seems that many TTS papers, even at Interspeech this year, continue to rely on MOS. In this work, the authors re-evaluated systems from the 2013 Blizzard challenge against each other, and modern architectures: Tacotron and FastPitch. They found that though the relative ranking of systems based on MOS was maintained, the true values of these scores changed, likely due to the introduction of more modern systems. As a result, in addition to reiterating the importance of not comparing scores from different evaluations to each other, they explicitly highlight that

"It is certainly not valid to simply pool MOS obtained from multiple listening tests, to form a training set for a MOS prediction model."

We are keen to see how further research in this area, such as with the VoiceMOS challenge, can improve MOS interpretability, or lead to even better subjective evaluation.

Visualising Model Training via Vowel Space for Text-To-Speech Systems

Explainability of how and what TTS models are learning during training can get less attention than it deserves. In this paper, Abeysinghe et al. specifically focus on the explainability of how a model can learn an accent, when it is initially trained on one accent (American English) and then fine tuned on another (New Zealand English). Throughout the fine-tuning stage, they

  1. generate samples using the latest iteration checkpoint
  2. run forced alignment on these samples to segment the vowels
  3. plot the average vowel space for each vowel

In this way, they are able to visualise how the model gradually unlearns the American English vowels as they transform into New Zealand English vowels. With this system, they are able to observe the most rapid change in earlier fine-tuning steps.

This work inspires us to think critically about other areas in which we could better understand how a TTS model is learning. Could we, for example, use a similar analysis to understand how multilingual TTS systems learn to distinguish between languages?

Investigating perception of spoken dialogue acceptability through surprisal

Finally, we wanted to discuss one of the best student papers, from CSTR in Edinburgh. Wallbridge et al. presented a very interesting investigation of surprisal in human conversation. Surprisal is a useful concept that relates to how un-predictable a linguistic segment is given the context, this has an impact on listener comprehension, among other things. In the experiments on Switchboard, human ability to judge surprisal was compared with judgements from a large language model. Their experiment demonstrated that humans do notice surprising dialogue responses. They explored a few approaches to judge surprisal with language model outputs, finding some level of correlation with human judgements. Finally, they explored which information was most useful in make these judgements. We’d be curious to see how this work could influence dialogue agents.

These were only some of the highlights that we saw at Interspeech this year. Stay tuned for our thorough blog post on TTS-specific contributions. Thanks again to the organisers for putting together such a great conference!

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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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