Search Results for author: Cheng-Tao Chung

Found 9 papers, 1 papers with code

Unsupervised Discovery of Structured Acoustic Tokens with Applications to Spoken Term Detection

no code implementations28 Nov 2017 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.

Hierarchical structure

Unsupervised Iterative Deep Learning of Speech Features and Acoustic Tokens with Applications to Spoken Term Detection

no code implementations17 Jul 2017 Cheng-Tao Chung, Cheng-Yu Tsai, Chia-Hsiang Liu, Lin-shan Lee

A Multi-granular Acoustic Tokenizer (MAT) was proposed for automatic discovery of multiple sets of acoustic tokens from the given corpus.

Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries

1 code implementation22 Mar 2017 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 phoneme boundaries.

An Iterative Deep Learning Framework for Unsupervised Discovery of Speech Features and Linguistic Units with Applications on Spoken Term Detection

no code implementations1 Feb 2016 Cheng-Tao Chung, Cheng-Yu Tsai, Hsiang-Hung Lu, Chia-Hsiang Liu, Hung-Yi Lee, Lin-shan Lee

The multiple sets of token labels are then used as the targets of a Multi-target Deep Neural Network (MDNN) trained on low-level acoustic features.

NTU System at MediaEval 2015: Zero Resource Query by Example Spoken Term Detection Using Deep and Recurrent Neural Networks

no code implementations MediaEval 2015 Workshop 2015 Cheng-Tao Chung, Yang-De Chen

This note serves as a documentation describing the methods the authors of this paper implemented for the Query by Example Search on Speech Task (QUESST) as a part of MediaEval 2015.

Keyword Spotting

Enhancing Automatically Discovered Multi-level Acoustic Patterns Considering Context Consistency With Applications in Spoken Term Detection

no code implementations7 Sep 2015 Cheng-Tao Chung, Wei-Ning Hsu, Cheng-Yi Lee, Lin-shan Lee

This paper presents a novel approach for enhancing the multiple sets of acoustic patterns automatically discovered from a given corpus.

Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity

no code implementations7 Sep 2015 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 corpus.

Unsupervised Discovery of Linguistic Structure Including Two-level Acoustic Patterns Using Three Cascaded Stages of Iterative Optimization

no code implementations7 Sep 2015 Cheng-Tao Chung, Chun-an Chan, Lin-shan Lee

This linguistic structure includes two-level (subword-like and word-like) acoustic patterns, the lexicon of word-like patterns in terms of subword-like patterns and the N-gram language model based on word-like patterns.

Language Modelling

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