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Word segmentation on discovered phone units with dynamic programming and self-supervised scoring
H Kamper - IEEE/ACM Transactions on Audio, Speech, and …, 2022 - ieeexplore.ieee.org
Recent work on unsupervised speech segmentation has used self-supervised models with
phone and word segmentation modules that are trained jointly. This paper instead revisits …
phone and word segmentation modules that are trained jointly. This paper instead revisits …
Segmenting continuous motions with hidden semi-markov models and gaussian processes
Humans divide perceived continuous information into segments to facilitate recognition. For
example, humans can segment speech waves into recognizable morphemes. Analogously …
example, humans can segment speech waves into recognizable morphemes. Analogously …
HVGH: unsupervised segmentation for high-dimensional time series using deep neural compression and statistical generative model
Humans perceive continuous high-dimensional information by dividing it into meaningful
segments, such as words and units of motion. We believe that such unsupervised …
segments, such as words and units of motion. We believe that such unsupervised …
Design and structure of the Juman++ morphological analyzer toolkit
An NLP tool is practical when it is fast in addition to having high accuracy. We describe the
architecture and the used methods to achieve 250× analysis speed improvement on the …
architecture and the used methods to achieve 250× analysis speed improvement on the …
Unsupervised word segmentation with bi-directional neural language model
L Wang, X Zheng - ACM Transactions on Asian and Low-Resource …, 2022 - dl.acm.org
We propose an unsupervised word segmentation model, in which for each unlabelled
sentence sample, the learning objective is to maximize the generation probability of the …
sentence sample, the learning objective is to maximize the generation probability of the …
A masked segmental language model for unsupervised natural language segmentation
Segmentation remains an important preprocessing step both in languages where" words" or
other important syntactic/semantic units (like morphemes) are not clearly delineated by white …
other important syntactic/semantic units (like morphemes) are not clearly delineated by white …
Sequence pattern extraction by segmenting time series data using GP-HSMM with hierarchical Dirichlet process
Humans recognize perceived continuous information by dividing it into significant segments
such as words and unit motions. We believe that such unsupervised segmentation is also an …
such as words and unit motions. We believe that such unsupervised segmentation is also an …
Learning word meanings and grammar for verbalization of daily life activities using multilayered multimodal latent Dirichlet allocation and Bayesian hidden Markov …
Intelligent systems need to understand and respond to human words to enable them to
interact with humans in a natural way. Several studies attempted to realize these abilities by …
interact with humans in a natural way. Several studies attempted to realize these abilities by …
Weakly supervised word segmentation for computational language documentation
Word and morpheme segmentation are fundamental steps of language documentation as
they allow to discover lexical units in a language for which the lexicon is unknown. However …
they allow to discover lexical units in a language for which the lexicon is unknown. However …
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages
CM Downey, S Drizin, L Haroutunian… - arxiv preprint arxiv …, 2021 - arxiv.org
We show that unsupervised sequence-segmentation performance can be transferred to
extremely low-resource languages by pre-training a Masked Segmental Language Model …
extremely low-resource languages by pre-training a Masked Segmental Language Model …