As the quality of contemporary speech synthesis improves, so too does the interest from language communities in developing text-to-speech (TTS) systems for a variety of real-world applications. Much of the work on TTS has focused on high-resource languages, resulting in implicitly resource-intensive paths to building such systems. The goal of this paper is to provide signposts and points of reference for future low-resource speech synthesis efforts, with insights drawn from the Speech Generation for Indigenous Language Education (SGILE) project. Funded and coordinated by the National Research Council of Canada (NRC), this multi-year, multi-partner project has the goal of producing high-quality text-to-speech systems that support the teaching of Indigenous languages in a variety of educational contexts. We provide background information and motivation for the project, as well as details about our approach and project structure, including results from a multi-day requirements-gathering session. We discuss some of our key challenges, including building models with appropriate controls for educators, improving model data efficiency, and strategies for low-resource transfer learning and evaluation. Finally, we provide a detailed survey of existing speech synthesis software and introduce EveryVoice TTS, a toolkit designed specifically for low-resource speech synthesis. • We provide background and points of reference for future low-resource TTS projects • We describe four main technical challenges for low-resource speech synthesis • We introduce the EveryVoice TTS Toolkit designed specifically for low-resource TTS • We compare the EveryVoice TTS Toolkit with six other existing toolkits

Self-supervised learning (SSL) representations from massively multilingual models offer a promising solution for low-resource language speech tasks. Despite advancements, language adaptation in TTS systems remains an open problem. This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system proposed in our previous work. We conducted experiments on 12 languages using limited data with various fine-tuning configurations. We demonstrate that the similarity in phonetics between the pretraining and target languages, as well as the language category, affects the target language’s adaptation performance. Additionally, we find that the fine-tuning dataset size and number of speakers influence adaptability. Surprisingly, we also observed that using paired data for fine-tuning is not always optimal compared to audio-only data. Beyond speech intelligibility, our analysis covers speaker similarity, language identification, and predicted MOS.

Global style tokens (GSTs) allow for rich modelling of the variation in a speech corpus and subsequent control of text-to-speech synthesis (TTS). However, certain styles of speech may be marked by variation along multiple dimensions, complicating the interpretation and control of learned style tokens. One example is hyperarticulated or ‘clear’ speech, for example as directed toward listeners with hearing impairments or language learners in the classroom, which in English is characterised by reduced speaking rate, increased F0, more careful articulation of vowels and plosive consonants, and other factors. We present a method for simplifying control of style tokens by applying principal components analysis (PCA) to GST weights from a TTS system trained on both plain and clear speech. We identify the axes of variation in PCA space with the acoustic correlates of clear speech in English and show that we can synthesise either style by moving along a single dimension in that space.

Mean Opinion Score (MOS) tests are the most widely used test type for subjective evaluation of speech samples. However, their use has been questioned, as results can vary significantly depending on the test material included. Forced-choice tests such as AB or Best Worst Scaling (BWS) can in principle mitigate some of these issues. Our aim here is to compare MOS, AB and BWS tests in 3 regards: 1) Which test type do listeners prefer in terms of ease, engagement and overall likeability? 2) How fast are listeners at each test type? 3) Does each test type provide the same pattern of results? To answer these questions we re-use a subset of stimuli from the Blizzard Challenge 2013 and conduct new MOS, AB and BWS tests. Overall, we conclude each test type is broadly equally valid, MOS may not in fact be the fastest or easiest test type for listeners, but the theoretical advantages of BWS are counterbalanced by it seeming less liked by our listeners here.

Neural text-to-speech (TTS) has achieved human-like synthetic speech for single-speaker, single-language synthesis. Multilingual TTS systems are limited to resource-rich languages due to the lack of large paired text and studio-quality audio data. TTS systems are typically built using a single speaker's voice, but there is growing interest in developing systems that can synthesize voices for new speakers using only a few seconds of their speech. This paper presents ZMM-TTS, a multilingual and multispeaker framework utilizing quantized latent speech representations from a large-scale, pre-trained, self-supervised model. Our paper combines text-based and speech-based self-supervised learning models for multilingual speech synthesis. Our proposed model has zero-shot generalization ability not only for unseen speakers but also for unseen languages. We have conducted comprehensive subjective and objective evaluations through a series of experiments. Our model has proven effective in terms of speech naturalness and similarity for both seen and unseen speakers in six high-resource languages. We also tested the efficiency of our method on two hypothetically low-resource languages. The results are promising, indicating that our proposed approach can synthesize audio that is intelligible and has a high degree of similarity to the target speaker's voice, even without any training data for the new, unseen language.

In this work we present an end-to-end pipeline for building a speech corpus and text-to-speech synthesis system for a new language without reference to any expert-defined linguistic resources. We segment and align over 85 hours of Scottish Gaelic recordings found online and select 2- and 8-hour subsets with comprehensive coverage of speech sounds based on self-supervised discrete acoustic unit sequences. We then compare FastPitch models trained on these relatively small data sets using character, acoustic unit and phone inputs. According to native speaker listening test judgements, characters serve well for Gaelic given its regular orthography, even in these limited data scenarios. We release our corpus building recipe so that others may easily apply our work to new languages.

This paper describes the motivation and development of speech synthesis systems for the purposes of language revitalization.By building speech synthesis systems for three Indigenous languages spoken in Canada, Kanien'kéha, Gitksan & SENĆOŦEN, we re-evaluate the question of how much data is required to build low-resource speech synthesis systems featuring state-of-the-art neural models.For example, preliminary results with English data show that a FastSpeech2 model trained with 1 hour of training data can produce speech with comparable naturalness to a Tacotron2 model trained with 10 hours of data.Finally, we motivate future research in evaluation and classroom integration in the field of speech synthesis for language revitalization.

We present an analysis of discrete units discovered via self-supervised representation learning on English speech. We focus on units produced by a pre-trained HuBERT model due to its wide adoption in ASR, speech synthesis, and many other tasks. Whereas previous work has evaluated the quality of such quantization models in aggregate over all phones for a given language, we break our analysis down into broad phonetic classes, taking into account specific aspects of their articulation when considering their alignment to discrete units. We find that these units correspond to sub-phonetic events, and that fine dynamics such as the distinct closure and release portions of plosives tend to be represented by sequences of discrete units. Our work provides a reference for the phonetic properties of discrete units discovered by HuBERT, facilitating analyses of many speech applications based on this model.