Eng Siong Chng

On statistical machine translation method for lexicon refinement in speech recognition

2015

In low resource Automatic Speech Recognition (ASR), one usually resorts to the Statistical Machine Translation (SMT) technique to learn transform rules to refine grapheme lexicon. To do this, we face two challenges. One is to …

Spoofing detection under noisy conditions: a preliminary investigation and an initial database

2016 · arXiv (Cornell University)

Spoofing detection for automatic speaker verification (ASV), which is to discriminate between live speech and attacks, has received increasing attentions recently. However, all the previous studies have been done on the clean data without significant …

Improving code-switching speech recognition with data augmentation and system combination

2019

We focused on a study of comprehensive approaches to an improved code-switching speech recognition, using data augmentation and system combination methods. For data augmentation, we not only use speech speed perturbation based method, but we …

Monolingual Data Selection Analysis for English-Mandarin Hybrid Code-switching Speech Recognition

2020 · arXiv (Cornell University)

In this paper, we conduct data selection analysis in building an English-Mandarin code-switching (CS) speech recognition (CSSR) system, which is aimed for a real CSSR contest in China. The overall training sets have three subsets, …

Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR

2023 · IEEE/ACM Transactions on Audio Speech and Language Processing

Automatic speech recognition (ASR) has gained remarkable successes thanks to recent advances of deep learning, but it usually degrades significantly under real-world noisy conditions. Recent works introduce speech enhancement (SE) as front-end to improve speech …

Are Soft Prompts Good Zero-Shot Learners for Speech Recognition?

2024

Large self-supervised pre-trained speech models require computationally expensive fine-tuning for downstream tasks. Soft prompt tuning offers a simple parameter-efficient alternative by utilizing minimal soft prompt guidance, enhancing portability while also maintaining competitive performance. However, not …

ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition Challenge

2024

To promote speech processing and recognition research in driving scenarios, we build on the success of the Intelligent Cockpit Speech Recognition Challenge (ICSRC) held at ISCSLP 2022 and launch the ICASSP 2024 In-Car Multi-Channel Automatic …

Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data

2019

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 propose a method to train an improved end-to-end …