问题:语音转录在诊断语音障碍(SSD)中至关重要,但容易受到转录者经验和感知偏见的影响。当前强制对准(FA)工具,注释音频文件以确定语音内容及其位置,通常需要手动转录,限制其有效性。方法:我们介绍一个小说,独立于文本的强制对齐模型,可以自主识别各个音素及其边界,解决这些限制。我们的方法利用了先进的,预训练的wav2vec2.0模型将语音分割成令牌并自动识别它们。为了准确识别音素边界,我们使用无监督分割工具,UnsupSeg.片段的标签采用最近邻分类,带有wav2vec2.0标签,在连接主义者时间分类(CTC)崩溃之前,基于最大重叠确定类标签。额外的后处理,包括过度的清洁和语音活动检测,是为了增强分割而实现的。结果:我们使用正常说话者的TIMIT数据集,将我们的模型与现有方法进行了基准测试,第一次,评估了其在包含SSD扬声器的TORGO数据集上的性能。我们的模型展示了有竞争力的表现,在TIMIT上达到76.88%和TORGO上达到70.31%的调和平均得分。含义:这项研究在SSD的评估和诊断方面取得了重大进展,提供比传统方法更客观、更少偏见的方法。我们的模型的有效性,特别是SSD扬声器,为言语病理学的研究和临床应用开辟了新的途径。
Problem: Phonetic transcription is crucial in diagnosing speech sound disorders (SSDs) but is susceptible to transcriber experience and perceptual bias. Current forced alignment (FA) tools, which annotate audio files to determine spoken content and its placement, often require manual transcription, limiting their effectiveness. Method: We introduce a novel, text-independent forced alignment model that autonomously recognises individual phonemes and their boundaries, addressing these limitations. Our approach leverages an advanced, pre-trained wav2vec 2.0 model to segment speech into tokens and recognise them automatically. To accurately identify phoneme boundaries, we utilise an unsupervised segmentation tool, UnsupSeg. Labelling of segments employs nearest-neighbour classification with wav2vec 2.0 labels, before connectionist temporal classification (CTC) collapse, determining class labels based on maximum overlap. Additional post-processing, including overfitting cleaning and voice activity detection, is implemented to enhance segmentation. Results: We benchmarked our model against existing methods using the TIMIT dataset for normal speakers and, for the first time, evaluated its performance on the TORGO dataset containing SSD speakers. Our model demonstrated competitive performance, achieving a harmonic mean score of 76.88% on TIMIT and 70.31% on TORGO. Implications: This research presents a significant advancement in the assessment and diagnosis of SSDs, offering a more objective and less biased approach than traditional methods. Our model\'s effectiveness, particularly with SSD speakers, opens new avenues for research and clinical application in speech pathology.