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Xiaosong Qiao

7 accepted papers

2025

Large Language Model Should Understand Pinyin for Chinese ASR Error Correction

ICASSP 2025accepted

Large language models (LLMs) can enhance automatic speech recognition (ASR) systems through generative error correction (GEC). In this paper, we propose Pinyin-enhanced GEC (PY-GEC), which leverages Pinyin—the phonetic representation of Mandarin Chinese—as supplementary information to improve Chines…

Cited by 0SourceScholar
2025

Multimodal Machine Translation with Text-Image In-depth Questioning

ACL 2025finding

Multimodal machine translation (MMT) integrates visual information to address ambiguity and contextual limitations in neural machine translation (NMT). Some empirical studies have revealed that many MMT models underutilize visual data during translation. They attempt to enhance cross-modal interacti…

2024

CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting

COLING 2024main

End-to-end automatic speech recognition (ASR) systems often struggle to recognize rare name entities, such as personal names, organizations and terminologies that are not frequently encountered in the training data. This paper presents Contextual Biasing Whisper (CB-Whisper), a novel ASR system base…

2024

Cross-Domain Audio Deepfake Detection: Dataset and Analysis

EMNLP 2024main

Audio deepfake detection (ADD) is essential for preventing the misuse of synthetic voices that may infringe on personal rights and privacy. Recent zero-shot text-to-speech (TTS) models pose higher risks as they can clone voices with a single utterance. However, the existing ADD datasets are outdated…

2023

SmartSpanNER: Making SpanNER Robust in Low Resource Scenarios

EMNLP 2023long findings

Named Entity Recognition (NER) is one of the most fundamental tasks in natural language processing. Span-level prediction (SpanNER) is more naturally suitable for nested NER than sequence labeling (SeqLab). However, according to our experiments, the SpanNER method is more sensitive to the amount of…

Cited by 0SourceScholar
2023

UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction

ICASSP 2023accepted

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data. But when only p…

Cited by 0SourceScholar