Goal: Numerous studies had successfully differentiated normal and abnormal
voice samples. Nevertheless, further classification had rarely been attempted.
This study proposes a novel approach, using continuous Mandarin speech instead
of a single vowel, to classify four common voice disorders (i.e. functional
dysphonia, neoplasm, phonotrauma, and vocal palsy). 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. 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. 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. Conclusions: The
results are consistent with other machine learning algorithms, including gated
recurrent units, random forest, deep neural networks, and LSTM. The
sensitivities for each disorder were also analyzed, and the model capabilities
were visualized via principal component analysis. An alternative experiment
based on a balanced dataset again confirms the advantages of using continuous
speech for learning voice disorders.

本研究提出一种新方法，使用连续的普通话语音而不是单个元音，来区分四种常见的声音障碍，并使用离散余弦变换和双向 LSTM 网络，通过大型数据库的实验表明，该方法相对于使用单个元音的系统有显著的准确度和召回率提高以及提高了系统学习声音障碍的能力。