16 citations · 18 across the 8 of their papers we have counts for
9 papers
Unsupervised Discovery of Structured Acoustic Tokens with Applications to Spoken Term Detection
Cheng-Tao Chung, Lin-Shan Lee
In this paper, we compare two paradigms for unsupervised discovery of structured acoustic tokens directly from speech corpora without any human annotation. The Multigranular Paradi…
Unsupervised Iterative Deep Learning of Speech Features and Acoustic Tokens with Applications to Spoken Term Detection
Cheng-Tao Chung, Cheng-Yu Tsai, Chia-Hsiang Liu +1
In this paper we aim to automatically discover high quality frame-level speech features and acoustic tokens directly from unlabeled speech data. A Multi-granular Acoustic Tokenizer…
Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data
Cheng-Kuan Wei, Cheng-Tao Chung, Hung-Yi Lee +1
It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not…
Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries
Yu-Hsuan Wang, Cheng-Tao Chung, Hung-yi Lee
In this paper we analyze the gate activation signals inside the gated recurrent neural networks, and find the temporal structure of such signals is highly correlated with the phone…
An Iterative Deep Learning Framework for Unsupervised Discovery of Speech Features and Linguistic Units with Applications on Spoken Term Detection
Cheng-Tao Chung, Cheng-Yu Tsai, Hsiang-Hung Lu +3
In this work we aim to discover high quality speech features and linguistic units directly from unlabeled speech data in a zero resource scenario. The results are evaluated using t…
Enhancing Automatically Discovered Multi-level Acoustic Patterns Considering Context Consistency With Applications in Spoken Term Detection
Cheng-Tao Chung, Wei-Ning Hsu, Cheng-Yi Lee +1
This paper presents a novel approach for enhancing the multiple sets of acoustic patterns automatically discovered from a given corpus. In a previous work it was proposed that diff…
Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity
Cheng-Tao Chung, Chun-an Chan, Lin-shan Lee
This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corp…
Unsupervised Discovery of Linguistic Structure Including Two-level Acoustic Patterns Using Three Cascaded Stages of Iterative Optimization
Cheng-Tao Chung, Chun-an Chan, Lin-shan Lee
Techniques for unsupervised discovery of acoustic patterns are getting increasingly attractive, because huge quantities of speech data are becoming available but manual annotations…
A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for Unsupervised Discovery of Linguistic Units and Generation of High Quality Features
Cheng-Tao Chung, Cheng-Yu Tsai, Hsiang-Hung Lu +5
This paper summarizes the work done by the authors for the Zero Resource Speech Challenge organized in the technical program of Interspeech 2015. The goal of the challenge is to di…