Search Results for author:
Found 8 papers, 1 papers with code
Audio-Visual Neural Syntax Acquisition
We study phrase structure induction from visually-grounded speech.
Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning
Cascaded SpeechCLIP attempted to generate localized word-level information and utilize both the pretrained image and text encoders.
Regularizing Contrastive Predictive Coding for Speech Applications
These representations significantly reduce the amount of labeled data needed for downstream task performance, such as automatic speech recognition.
Guided contrastive self-supervised pre-training for automatic speech recognition
Contrastive Predictive Coding (CPC) is a representation learning method that maximizes the mutual information between intermediate latent representations and the output of a given model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition
In this paper, we 1) investigate the influence of different factors (i. e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language; 2) provide an analysis of which phones transfer well across languages and which do not in order to understand the limitations of and areas for further improvement for automatic phone inventory creation; and 3) present different methods to build a phone inventory of an unseen language in an unsupervised way.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding
We overcome this limitation with a segmental contrastive predictive coding (SCPC) framework to model the signal structure at a higher level, e. g., phone level.
Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation
We overcome this limitation with a segmental contrastive predictive coding (SCPC) framework that can model the signal structure at a higher level e. g. at the phoneme level.
Self-Expressing Autoencoders for Unsupervised Spoken Term Discovery
We perform segmentation based on the assumption that the frame feature vectors are more similar within a segment than across the segments.
