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Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenarios. It is likewise difficult to apply this to dialects and languages for which only limited labeled data is available. Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Such methods have shown success in natural language processing and computer vision domains, achieving new levels of performance while reducing the number of labels required for many downstream scenarios. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. Other approaches rely on multi-modal data for pre-training, mixing text or visual data streams with speech. Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years. This review presents approaches for self-supervised speech representation learning and their connection to other research areas. Since many current methods focus solely on automatic speech recognition as a downstream task, we review recent efforts on benchmarking learned representations to extend the application beyond speech recognition.
For a long time, language technology has been developed principally using large quantities of textual resources. This makes sense, since, as far as technological applications are concerned, language has primarily been used in written form. When it comes to dealing with spoken language, however, this has given rise to a division of labor between, on the one hand, speech components which aim at converting speech to text or text to speech (ASR, automatic speech recognition, and TTS, text-to-speech synthesis), and, on the other hand, components that perform a variety of language tasks based on text (language understanding, dialogue, language generation). As a result, even speech-first applications like speech-to-speech translation or speech assistants like Alexa or Siri are cobbled together in a Frankensteinian fashion, with some components trained on text and others trained on speech (see Fig. 1(a))—and with all the speech components trained using large amounts of supervision (textual transcription) so that they can communicate with the text-based components. But is this a necessity? Could we build spoken-language based applications directly from the audio stream without using any text?