The previous works combined acoustic signals and medical records Personal habits and behaviors, such as laryngeal tumors caused by long-term use of tobacco and alcohol, were studied in. The running speech from both English and Arabic databases was considered as well in, and the associated speech features were evaluated in terms of voice-disorder classifiers. The voice disorder classification, and voice quality measures. Therefore, sustained vowels were investigated for One possible reason may be the limitation of the single vowel speech signal. This competition established a systematic evaluation methodology with rigorous metricsįor the comparison of voice disorders detection in fair conditions, and over one hundred teams participated in this challengeĪlthough numerous published studies had successfully differentiated normal and abnormal voice samples, further classification has rarely been attempted. The IEEE Big Data conference held an international competition in Seattle 2018, called FEMH-Challenge, in which voice pathology detection systems from different research groups worldwide are evaluated empirically on the same dataset, which was published by Far Eastern Memorial Hospital (FEMH), Taiwan. Various acoustic features, including cepstral features ,Īnd entropy were also investigated in the literature. The recent works applied unsupervised domain adaptation to address the hardware variation. In addition,ĭeep learning and convolutional neural networks were investigated for pathological voice detection. The work in performed correlation analyses on the sub-band signal to detect pathological voices. I INTRODUCTIONįor example, the works in and extracted vocal-fold-related acoustic features and combined them with a support vector machine for voice pathology detection. Future practice can screen patients who truly need hospital visits and reduce unnecessary medical demands, especially during COVID-19 pandemic. Impact Statement- Deep learning can detect common voice disorders using continuous speech. Index Terms- Pathological voice, diseases classification, acoustic signal, artificial intelligence. The sensitivities for each disorder were also analyzed, and the model capabilities were visualized via principal component analysis.Īn alternative experiment based on a balanced dataset again confirms the advantages of using continuous speech for learning voice disorders. Conclusions : The results are consistent with other machine learning algorithms, including gated recurrent units, random forest, deep neural networks, and LSTM. Results: Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall improvements of 78.12–89.27% and 50.92–80.68%, respectively, compared with systems that use a single vowel. The experiments were conducted on a large-scale database, wherein 1,045 continuous speech were collected by the speech clinic of a hospital from 2012 to 2019. Methods: In the proposed framework, acoustic signals are transformed into mel-frequency cepstral coefficients, and a bi-directional long-short term memory network (BiLSTM) is adopted to model the sequential features. functional dysphonia, neoplasm, phonotrauma, and vocal palsy). This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to classify four common voice disorders (i.e. Nevertheless, further classification had rarely been attempted. Goal: Numerous studies had successfully differentiated normal and abnormal voice samples.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |