Search Results for author: Hai-Hua Xu

Found 9 papers, 2 papers with code

Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems

no code implementations18 May 2020 Tingzhi Mao, Yerbolat Khassanov, Van Tung Pham, Hai-Hua Xu, Hao Huang, Eng Siong Chng

In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance.

Language Modelling

Spatial-Scale Aligned Network for Fine-Grained Recognition

no code implementations5 Jan 2020 Lizhao Gao, Hai-Hua Xu, Chong Sun, Junling Liu, Yu-Wing Tai

Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance.

Fine-Grained Visual Recognition

Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation

1 code implementation8 Apr 2019 Yerbolat Khassanov, Zhiping Zeng, Van Tung Pham, Hai-Hua Xu, Eng Siong Chng

However, learning the representation of rare words is a challenging problem causing the NLM to produce unreliable probability estimates.

speech-recognition Speech Recognition

On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition

1 code implementation1 Nov 2018 Zhiping Zeng, Yerbolat Khassanov, Van Tung Pham, Hai-Hua Xu, Eng Siong Chng, Haizhou Li

Code-switching (CS) refers to a linguistic phenomenon where a speaker uses different languages in an utterance or between alternating utterances.

Data Augmentation Language Identification +3

Study of Semi-supervised Approaches to Improving English-Mandarin Code-Switching Speech Recognition

no code implementations16 Jun 2018 Pengcheng Guo, Hai-Hua Xu, Lei Xie, Eng Siong Chng

In this paper, we present our overall efforts to improve the performance of a code-switching speech recognition system using semi-supervised training methods from lexicon learning to acoustic modeling, on the South East Asian Mandarin-English (SEAME) data.

speech-recognition Speech Recognition

Fantastic 4 system for NIST 2015 Language Recognition Evaluation

no code implementations5 Feb 2016 Kong Aik Lee, Ville Hautamäki, Anthony Larcher, Wei Rao, Hanwu Sun, Trung Hieu Nguyen, Guangsen Wang, Aleksandr Sizov, Ivan Kukanov, Amir Poorjam, Trung Ngo Trong, Xiong Xiao, Cheng-Lin Xu, Hai-Hua Xu, Bin Ma, Haizhou Li, Sylvain Meignier

This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE).


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