Question: What model and classifier are used to classify utterances from PD patients and healthy control subjects?
Evidence: Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the mid-brain producing several motor and non-motor impairments in the patients BIBREF0. Motor symptoms include among others, bradykinesia, rigidity, resting tremor, micrographia, and different speech impairments. The speech impairments observed in PD patients are typically grouped as hypokinetic dysarthria, and include symptoms such as vocal folds rigidity, bradykinesia, and reduced control of muscles and limbs involved in the speech production. The effects of dysarthria in the speech of PD patients include increased acoustic noise, reduced intensity, harsh and breathy voice quality, increased voice nasality, monopitch, monoludness, speech rate disturbances, imprecise articulation of consonants BIBREF1, and involuntary introduction of pauses BIBREF2. Clinical observations in the speech of patients can be objectively and automatically measured by using computer aided methods supported in signal processing and pattern recognition with the aim to address two main aspects: (1) to support the diagnosis of the disease by classifying healthy control (HC) subjects and patients, and (2) to predict the level of degradation of the speech of the patients according to a specific clinical scale.
Most of the studies in the literature to classify PD from speech are based on computing hand-crafted features and using classifiers such as support vector machines (SVMs) or K-nearest neighbors (KNN). For instance, in BIBREF3, the authors computed features related to perturbations of the fundamental frequency and amplitude of the speech signal to classify utterances from 20 PD patients and 20 HC subjects, Turkish speakers. Classifiers based on KNN and SVMs were considered, and accuracies of up to 75% were reported. Later, in BIBREF4 the authors proposed a phonation analysis based on several time frequency representations to assess tremor in the speech of PD patients. The extracted features were based on energy and entropy computed from time frequency representations. Several classifiers were used, including Gaussian mixture models (GMMs) and SVMs. Accuracies of up to 77% were reported in utterances of the PC-GITA database BIBREF5, formed with utterances from 50 PD patients and 50 HC subjects, Colombian Spanish native speakers. The authors from BIBREF6 computed features to model different articulation deficits in PD such as vowel quality, coordination of laryngeal and supra-laryngeal activity, precision of consonant articulation, tongue movement, occlusion weakening, and speech timing. The authors studied the rapid repetition of the syllables /pa-ta-ka/ pronounced by 24 Czech native speakers, and reported an accuracy of 88% discriminating between PD patients and HC speakers, using an SVM classifier. Additional articulation features were proposed in BIBREF7, where the authors modeled the difficulty of PD patients to start/stop the vocal fold vibration in continuous speech. The model was based on the energy content in the transitions between unvoiced and voiced segments. The authors classified PD patients and HC speakers with speech recordings in three different languages (Spanish, German, and Czech), and reported accuracies ranging from 80% to 94% depending on the language; however, the results were optimistic, since the hyper-parameters of the classifier were optimized based on the accuracy on the test set. Another articulation model was proposed in BIBREF8. The authors considered a forced alignment strategy to segment the different phonetic units in the speech utterances. The phonemes were segmented and grouped to train different GMMs. The classification was performed based on a threshold of the difference between the posterior probabilities from the models created for HC subjects and PD patients. The model was tested with Colombian Spanish utterances from the PC-GITA database BIBREF5 and with the Czech data from BIBREF9. The authors reported accuracies of up to 81% for the Spanish data, and of up to 94% for the Czech data.
In addition to the hand-crafted feature extraction models, there is a growing interest in the research community to consider deep learning models in the assessment of the speech of PD patients BIBREF10, BIBREF11, BIBREF12. Deep learning methods have the potential to extract more abstract and robust features than those manually computed. These features could help to improve the accuracy of different models to classify pathological speech, such as PD BIBREF13. A deep learning based articulation model was proposed in BIBREF11 to model the difficulties of the patients to stop/start the vibration of the vocal folds. Transitions between voiced and unvoiced segments were modeled with time-frequency representations and convolutional neural networks (CNNs). The authors considered speech recordings of PD patients and HC speakers in three languages: Spanish, German, and Czech, and reported accuracies ranging from 70% to 89%, depending on the language. However, in a language independent scenario, i.e., training the CNN with utterances from one language and testing with the remaining two, the results were not satisfactory (accuracy$<60\%$).
The classification of PD from speech in different languages has to be carefully conducted to avoid bias towards the linguistic content present in each language. For instance, Czech and German languages are richer than Spanish language in terms of consonant production, which may cause that it is easier to produce consonant sounds by Czech PD patients than by Spanish PD patients. Despite these language dependent issues, the results in the classification of PD in different languages could be improved using a transfer learning strategy among languages, i.e., to train a base model with utterances from one language, and then, to perform a fine-tuning of the weights with utterances from the target language BIBREF14. Similar approaches based on transfer learning have been recently considered to classify PD using handwriting BIBREF15. In the present study, we propose a methodology to classify PD via a transfer learning strategy with the aim to improve the accuracy in different languages. CNNs trained with utterances from one language are used to initialize a model to classify speech utterances from PD patients in a different language. The models are evaluated with speech utterances in Spanish, German, and Czech languages. The results suggest that the use of a transfer learning strategy improved the accuracy of the models over 8% with respect to those obtained when the model is trained only with utterance from the target language.
The extracted features were based on energy and entropy computed from time frequency representations. Several classifiers were used, including Gaussian mixture models (GMMs) and SVMs. Accuracies of up to 77% were reported in utterances of the PC-GITA database [6], formed with utterances from 50 PD patients and 50 HC subjects, Colombian Spanish native speakers. The authors from [7] computed features to model different articulation deficits in PD such as vowel quality, coordination of laryngeal and supra-laryngeal activity, precision of consonant articulation, tongue movement, occlusion weakening, and speech timing. The authors studied the rapid repetition of the syllables /pa-ta-ka/ pronounced by 24 Czech native speakers, and reported an accuracy of 88% discriminating between PD patients and HC speakers, using an SVM classifier.
Answer: A Gaussian mixture model (GMMs) and SVMs.