823-OLT @ BUET DL Sprint 4.0：基于上下文的窗口化技术用于印地语长音频的自动语音识别与细调说话人识别
823-OLT @ BUET DL Sprint 4.0: Context-Aware Windowing for ASR and Fine-Tuned Speaker Diarization in Bengali Long Form Audio
AI总结：
本研究提出基于上下文的窗口化技术，用于改进印地语长音频的自动语音识别与说话人识别，通过结合语音分离和细调模型提升转录效率与说话人识别准确性。
摘要：
尽管印地语是全球最广泛使用的语言之一，但在长音频语音技术中仍然处于代表性不足的状态，尤其是在涉及转录和说话人识别的系统中。我们提出了针对长音频印地语语音智能的框架，采用基于Whisper Medium的模型进行自动语音识别，以及使用细调分割模型进行说话人识别。ASR流程结合了语音分离、语音活动检测和一个间隙感知的窗口化策略，以构建保留上下文的片段以实现稳定的解码。在说话人识别方面，一个预训练的说话人分割模型在官方竞赛数据集上进行微调（该数据集作为BUET CSE Fest下的DL Sprint 4.0竞赛的一部分提供），以更好地捕捉印地语对话模式。最终系统能够高效地转录长音频并提供说话人感知的转录，为低资源语言提供可扩展的语音技术解决方案。
英文摘要：
Bengali, despite being one of the most widely spoken languages globally, remains underrepresented in long form speech technology, particularly in systems addressing transcription and speaker attribution. We present frameworks for long form Bengali speech intelligence that address automatic speech recognition using a Whisper Medium based model and speaker diarization using a finetuned segmentation model. The ASR pipeline incorporates vocal separation, voice activity detection, and a gap aware windowing strategy to construct context preserving segments for stable decoding. For diarization, a pretrained speaker segmentation model is finetuned on the official competition dataset (provided as part of the DL Sprint 4.0 competition organized under BUET CSE Fest), to better capture Bengali conversational patterns. The resulting systems deliver both efficient transcription of long form audio and speaker aware transcription to provide scalable speech technology solutions for low resource languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
