A crucial step in language acquisition is the segmentation of the continuous speech signals into words or phonemes without explicit boundaries and labels. For example, infants discover the boundaries between words during language acquisition and can adjust these boundaries using their knowledge of words [6,7].
In this study, we propose a novel probabilistic generative model (PGM) that can learn phonemes, words, and grammar from continuous speech signals.
Humans can acquire language from speech signals that are produced by others [1]-[5]. Segmenting continuous speech signals into words and phonemes without clear ...
Humans can divide the perceived continuous speech signals, which exhibit double articulation structure, into phonemes and words without explicit boundary points ...
Missing: Grammar | Show results with:Grammar
Aug 30, 2024 · We analyse the behaviour of Incremental Model-Based Clustering on child-directed speech data, and suggest a possible use of this method to ...
Unsupervised phoneme and word acquisition from continuous speech based on a hierarchical probabilistic generative model · No full-text available · Citations (2) ...
Unsupervised Acquisition of Phonemes, Words, and Grammar from Continuous Speech Signals. Shoma Ochiai 1. ,. Masatoshi Nagano 1. ,. Tomoaki Nakamura 1.
Unsupervised phoneme and word acquisition from continuous speech based on a hierarchical probabilistic generative model · Masatoshi Nagano, Tomoaki Nakamura ...
Missing: Grammar | Show results with:Grammar
We present an unsupervised learning algorithm that acquires a natural-language lexicon from raw speech. The algorithm is based on the optimal encoding of ...
A third instantiation of the framework explores phonetic and acoustic extensions; the resulting model is used to learn words directly from continuous speech.