论文标题
命名为音频识别的实体识别
Named Entity Recognition for Audio De-Identification
论文作者
论文摘要
数据匿名通常是人类执行的任务。自动化它将减少完成此任务所需的成本和时间。本文提出了一条管道，以自动化法文音频数据的匿名化。我们提出了一条管道，该管道将音频文件带有其抄录，并删除音频中存在的命名实体（NES）。我们的管道由强制对准器组成，该对准器将音频成绩单中的单词与语音和表现命名实体识别（NER）的模型保持一致。然后，将与NES相对应的音频段用沉默代替为匿名音频。我们比较了强迫对准器和NER模型，以找到最适合我们的场景的模型。我们在一个小型手持数据集上评估了管道，达到0.769的F1分数。该结果表明，自动执行此任务是可行的。
Data anonymization is often a task carried out by humans. Automating it would reduce the cost and time required to complete this task. This paper presents a pipeline to automate the anonymization of audio data in French. We propose a pipeline, which takes audio files with their transcriptions and removes the named entities (NEs) present in the audio. Our pipeline is made up of a forced aligner, which aligns words in an audio transcript with speech and a model that performs named entity recognition (NER). Then, the audio segments that correspond to NEs are substituted with silence to anonymize audio. We compared forced aligners and NER models to find the best ones for our scenario. We evaluated our pipeline on a small hand-annotated dataset, achieving an F1 score of 0.769. This result shows that automating this task is feasible.
