2022, End-to-End Audio-Visual Neural Speaker Diarization [2022, Interspeech]
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Abstract
- multimodal inputs
- uses audio features, lip regions of interest, and i-vector embeddings
- I-vectors are the key point to solve the alignment problem caused by visual modality errors
- e.g., occlusions, off-screen speakers, or unreliable detection
- Our audio-visual model is robust to the absence of visual modality, where the diarization performance degrades significantly using the visual-only model
- It is robust to visual modality errors and outperforms audio-only and video-only systems
Introudction
- exploring the effects of lip motion and speech on speaker diarization using high-definition lip ROIs and single-channel audios
- By manually removing lip ROI fragments, we can compare the impact of different degrees of lip misalignment on speaker diarization.
Currently pursuing my Ph.D. in GIST, I am deeply intrigued by the field of speaker diarization and committed to making meaningful contributions to it.
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2024년 4월 14일
Neural speaker diarization is a cutting-edge technology in speech processing that involves the identification and separation of individual speakers within an audio recording. This technology uses neural networks to analyze the acoustic features of speech, such as pitch and tone, making it possible to attribute spoken words to distinct speakers accurately. While the concept might seem as specialized as shopping for rzr xp1000 accessories in the world of off-road vehicles, its applications are broad, ranging from enhancing voice assistants to improving transcription accuracy in multi-speaker environments. This approach improves over traditional methods by adapting more dynamically to varied speech patterns and overlapping conversations.
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