Applying Random Forest classification to diagnose autism using acoustical voice-quality parameters during lexical tone production. (August 2022)

• Record Type: Journal Article
• Authors: Guo, Chengyu; Chen, Fei; Chang, Yajie; Yan, Jinting
• Is Part Of: Biomedical signal processing and control. Volume 77(2022)
• Publication Date: 2022-08
• Subjects: Autism -- Random Forest classification -- Voice quality -- Lexical tone production; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103811
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Random Forest classification was used to diagnose ASD based on acoustical voice-quality parameters. Mandarin-speaking children with ASD tended to overexert and overstrain their voices. Shimmer and jitter might be robust acoustic parameters in discriminating voice quality in ASD. Voice quality has the potential and supplementary value for diagnosing ASD. Abstract: Atypical voice quality has been reported among children with autism spectrum disorder (ASD). Yet, it is unclear which acoustic parameters played a crucial role in discriminating the voice of children with ASD from that of their typically developing (TD) peers, especially those who speak a tone language. The current study carried out a preliminary investigation of voice quality in Mandarin-speaking children with ASD using multidimensional acoustic parameters, in an effort to seek the most robust cues using the Random Forest classification. Twenty Mandarin-speaking children with ASD and twenty age-matched TD children participated in the lexical tone production using a picture-naming task. Acoustic parameters included in this study were time-domain parameters: fundamental frequency (F0), the range of F0, the strength of excitation; spectral parameters: H1*-H2*, H2*-H4*, H1*-A1*, H1*-A2*, H1*-A3*; and signal aperiodicity parameters: cepstral peak prominence, harmonic-to-noise ratio, subharmonic-to-harmonic ratio, jitter, and shimmer. Results showed that except for HNR and F0 range, group differences (ASD vs. TD) were found in the other 11 parameters. Additionally, a 78.5% accuracy rate was obtained for classification analysis between voice-quality features of children with and without ASD, with shimmer and jitter as robust parameters. These results indicated that Mandarin-speaking children with ASD tended to overexert and overstrained their voices. Especially for Tone 3 production, they notably exhibited a higher F0 with a less creaky voice, losing the typical voice-quality feature of T3. Although no voice disorders were detected among Mandarin-speaking children with ASD, voice quality has the potential and supplementary value for diagnosing ASD.