Self-supervised contrastive learning for unsupervised phoneme segmentation
We propose a self-supervised representation learning model for the task of unsupervised
phoneme boundary detection. The model is a convolutional neural network that operates …
phoneme boundary detection. The model is a convolutional neural network that operates …
Computational modeling of phonetic and lexical learning in early language acquisition: Existing models and future directions
O Räsänen - Speech Communication, 2012 - Elsevier
This work reviews a number of existing computational studies concentrated on the question
of how spoken language can be learned from continuous speech in the absence of …
of how spoken language can be learned from continuous speech in the absence of …
What do self-supervised speech models know about words?
Many self-supervised speech models (S3Ms) have been introduced over the last few years,
improving performance and data efficiency on various speech tasks. However, these …
improving performance and data efficiency on various speech tasks. However, these …
Unsupervised speech recognition via segmental empirical output distribution matching
We consider the problem of training speech recognition systems without using any labeled
data, under the assumption that the learner can only access to the input utterances and a …
data, under the assumption that the learner can only access to the input utterances and a …
Phoneme boundary detection using learnable segmental features
Phoneme boundary detection plays an essential first step for a variety of speech processing
applications such as speaker diarization, speech science, keyword spotting, etc. In this work …
applications such as speaker diarization, speech science, keyword spotting, etc. In this work …
Blind phoneme segmentation with temporal prediction errors
Phonemic segmentation of speech is a critical step of speech recognition systems. We
propose a novel unsupervised algorithm based on sequence prediction models such as …
propose a novel unsupervised algorithm based on sequence prediction models such as …
A computational model of word segmentation from continuous speech using transitional probabilities of atomic acoustic events
O Räsänen - Cognition, 2011 - Elsevier
Word segmentation from continuous speech is a difficult task that is faced by human infants
when they start to learn their native language. Several studies indicate that infants might use …
when they start to learn their native language. Several studies indicate that infants might use …
Blind phone segmentation based on spectral change detection using Legendre polynomial approximation
DT Hoang, HC Wang - The Journal of the Acoustical Society of …, 2015 - pubs.aip.org
Phone segmentation involves partitioning a continuous speech signal into discrete phone
units. In this paper, a method for automatic phone segmentation without prior knowledge of …
units. In this paper, a method for automatic phone segmentation without prior knowledge of …
Blind speech segmentation using spectrogram image-based features and mel cepstral coefficients
This paper introduces a novel method for blind speech segmentation at a phone level based
on image processing. We consider the spectrogram of the waveform of an utterance as an …
on image processing. We consider the spectrogram of the waveform of an utterance as an …
Automatic segmentation and classification of dysfluencies in stuttering speech
The automatic segmentation of pathological speech has gained importance in the field of
clinical speech processing. In the disorder like stuttering, dysfluencies present in continues …
clinical speech processing. In the disorder like stuttering, dysfluencies present in continues …