Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition
Phonemes are defined by their relationship to words: changing a phoneme changes the word. Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to under-resourced speech technology. In this paper, we bridge the gap between the linguistic and statistical definition of phonemes and propose a novel neural discrete representation learning model for self-supervised learning of phoneme inventory with raw speech and word labels. Under mild assumptions, we prove that the phoneme inventory learned by our approach converges to the true one with an exponentially low error rate. Moreover, in experiments on TIMIT and Mboshi benchmarks, our approach consistently learns a better phoneme-level representation and achieves a lower error rate in a zero-resource phoneme recognition task than previous state-of-the-art self-supervised representation learning algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Phoneme RecognitionRepresentation LearningSelf-Supervised Learningspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition
Phonemes are defined by their relationship to words: changing a phoneme changes the word. Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to under-resour…
Phoneme RecognitionRepresentation LearningSelf-Supervised Learningspeech-recognition+1SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in HuBERT
Data-driven unit discovery in self-supervised learning (SSL) of speech has embarked on a new era of spoken language processing. Yet, the discovered units often remain in phonetic space and the units beyond phonemes are l…
Language ModelingLanguage ModellingSelf-Supervised LearningSentenceDo speech foundation models really learn words?
Self-supervised speech foundation models are now used in a wide array of downstream applications, including traditional speech recognition and as the basis for tokens in speech-aware language models. Attempts to understa…
Speech RecognitionData-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system. Most of the current G2P systems rely on carefully hand-crafted lexicons developed by experts. This poses a two…
Self-Supervised Learningtext-to-speechText to SpeechDiscoPhon: Benchmarking the Unsupervised Discovery of Phoneme Inventories With Discrete Speech Units
We introduce DiscoPhon, a multilingual benchmark for evaluating unsupervised phoneme discovery from discrete speech units. DiscoPhon covers 6 dev and 6 test languages, chosen to span a wide range of phonemic contrasts. G…