Abstract
In this report, we summarize the integrated multilingual audio processing pipeline developed by our team for the inaugural NCIIPC Startup India AI GRAND CHALLENGE, addressing Problem Statement 06: Language-Agnostic Speaker Identification and Diarisation, and subsequent Transcription and Translation System. Our primary focus was on advancing speaker diarization, a critical component for multilingual and code-mixed scenarios. The main intent of this work was to study the real-world applicability of our in-house speaker diarization (SD) systems. To this end, we investigated a robust voice activity detection (VAD) technique and fine-tuned speaker embedding models for improved speaker identification in low-resource settings. We leveraged our own recently proposed multi-kernel consensus spectral clustering framework, which substantially improved the diarization performance across all recordings in the training corpus provided by the organizers. Complementary modules for speaker and language identification, automatic speech recognition (ASR), and neural machine translation were integrated in the pipeline. Post-processing refinements further improved system robustness.
Abstract (translated)
在本报告中，我们总结了团队为首次NCIIPC Startup India AI GRAND CHALLENGE开发的集成多语言音频处理管道，该挑战针对问题陈述06：无语言障碍的说话人识别和会议记录（Diarisation），以及随后的转录和翻译系统。我们的主要关注点是推进说话人身份验证，在多语言和代码混合场景中这是关键组件。这项工作的主要目的是研究我们内部开发的说话人身份验证(SD)系统的实际应用可行性。为此，我们调查了一种稳健的声音活动检测(VAD)技术，并对说话人嵌入模型进行了微调，以在资源匮乏的情况下提高说话人的识别精度。我们利用了自己最近提出的多核共识谱聚类框架，这显著提高了训练语料库中所有录音的会议记录性能。此外，我们将用于说话人和语言识别、自动语音识别(ASR)及神经机器翻译的互补模块整合到了处理管道中。后处理改进进一步增强了系统的稳健性。
URL
https://arxiv.org/abs/2512.11009
