20 citations · 86 across the 25 of their papers we have counts for
8 papers · 1 filter
From Oracle to Noisy Context: Mitigating Contextual Exposure Bias in Speech-LLMs
Xiaoyong Guo, Nanjie Li, Zijie Zeng +4
Contextual automatic speech recognition (ASR) with Speech-LLMs is typically trained with oracle conversation history, but relies on error-prone history at inference, causing a trai…
Internal Language Model Estimation based Adaptive Language Model Fusion for Domain Adaptation
Rao Ma, Xiaobo Wu, Jin Qiu +4
ASR model deployment environment is ever-changing, and the incoming speech can be switched across different domains during a session. This brings a challenge for effective domain a…
Independent language modeling architecture for end-to-end ASR
Van Tung Pham, Haihua Xu, Yerbolat Khassanov +5
The attention-based end-to-end (E2E) automatic speech recognition (ASR) architecture allows for joint optimization of acoustic and language models within a single network. However,…
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data
Yerbolat Khassanov, Haihua Xu, Van Tung Pham +4
The lack of code-switch training data is one of the major concerns in the development of end-to-end code-switching automatic speech recognition (ASR) models. In this work, we propo…
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
Yerbolat Khassanov, Zhiping Zeng, Van Tung Pham +2
The neural language models (NLM) achieve strong generalization capability by learning the dense representation of words and using them to estimate probability distribution function…
On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition
Zhiping Zeng, Yerbolat Khassanov, Van Tung Pham +3
Code-switching (CS) refers to a linguistic phenomenon where a speaker uses different languages in an utterance or between alternating utterances. In this work, we study end-to-end…
Study of Semi-supervised Approaches to Improving English-Mandarin Code-Switching Speech Recognition
Pengcheng Guo, Haihua Xu, Lei Xie +1
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…
Fantastic 4 system for NIST 2015 Language Recognition Evaluation
Kong Aik Lee, Ville Hautamäki, Anthony Larcher +14
This article describes the systems jointly submitted by Institute for Infocomm (IR), the Laboratoire d'Informatique de l'Université du Maine (LIUM), Nanyang Technology Universi…