[1] Harár P, Burget R, Dutta MK. “Speech emotion recognition with deep learning,” in 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN) (Noida: IEEE), 2017; 137-140.
[2] Alghifari MF, Gunawan TS, Qadri SA, Kartiwi M, Janin Z. On the use of voice activity detection in speech emotion recognition. Bull. Elect. Eng. Inf. 2019;8: 1324-1332. doi: 10.11591/eei.v8i4.1646
[3] Akçay MB, Oğuz K. Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 2020; 116: 56-76. doi: 10.1016/j.specom.2019.12.001
[4] Baird A, Amiriparian S, Cummins N, Alcorn AM, Batliner A, Pugachevskiy S, et al. “Automatic Classification of autistic child vocalisations: a novel database and results,” in Proceedings of the Interspeech 2017 (Stockholm), 2017; 849-853.
[5] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5), Arlington, VA: APA. 2013.
[6] Kopp S, Beckung E, Gillberg C. Developmental coordination disorder and other motor control problems in girls with autism spectrum disorder and/or attention-deficit/hyperactivity disorder. Res. Develop. Disabil. 2010; 31: 350-361. doi: 10.1016/j.ridd.2009.09.017
[7] Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J. Autism spectrum disorder. Lancet 2018; 392: 508-520. doi: 10.1016/S0140-6736(18)31129-2
[8] Hudson CC, Hall L, Harkness KL. Prevalence of depressive disorders in individuals with autism spectrum disorder: A meta-analysis. J. Abnormal Child Psychol. 2019; 47: 165-175. doi: 10.1007/s10802-018-0402-1
[9] Zaboski BA, Storch EA. Comorbid autism spectrum disorder and anxiety disorders: a brief review. Future Neurol. 2018; 13: 31-37. doi: 10.2217/fnl-2017-0030
[10] Schuller B, Steidl S, Batliner A, Vinciarelli A, Scherer K, Ringeval F, et al. “The interspeech 2013 computational paralinguistics challenge: Social signals, conflict, emotion, autism,” in Proceedings Interspeech 2013, 14th Annual Conference of the International Speech Communication Association (Lyon). 2013.
[11] Nahar R, Kai A. “Effect of data augmentation on dnn-based vad for automatic speech recognition in noisy environment,” in 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) Kobe, 2020; 368-372.
[12] Amiriparian S, Baird A, Julka S, Alcorn A, Ottl S, Petrović S, et al. “Recognition of echolalic autistic child vocalisations utilising convolutional recurrent neural networks,” in Proceedings of the Interspeech 2018 (Hyderabad), 2018; 2334-2338
[13] Schadenberg BR, Reidsma D, Evers V, Davison DP, Li JJ, Heylen DK, et al. Predictable robots for autistic children-variance in robot behaviour, idiosyncrasies in autistic children's characteristics, and child–robot engagement. ACM Trans. Comput. Human Interact. 2021; 28: 1-42. doi: 10.1145/3468849
[14] Schuller BW. Speech emotion recognition: two decades in a nutshell, benchmarks, and ongoing trends. Commun. ACM 61, 2018; 90-99. doi: 10.1145/3129340
[15] Shen J, Ainger E, Alcorn A, Dimitrijevic SB, Baird A, Chevalier P, et al. Autism data goes big: A publicly-accessible multi-modal database of child interactions for behavioural and machine learning research. In International Society for Autism Research Annual Meeting (Kansas City, MO). 2018.
[16] Howlin P, Baron-Cohen S, Hadwin J. Teaching Children With Autism to Mind-Read: A Practical Guide for Teachers and Parents. Chichester: J. Wiley & Sons Chichester. 1999.
[17] Ringeval F, Schuller B, Valstar M, Cummins N, Cowie R, Tavabi L, et al. “Avec 2019 workshop and challenge: state-of-mind, detecting depression with ai, and cross-cultural affect recognition,” in Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop AVEC '19 (New York, NY: Association for Computing Machinery), 2019; 3-12.
[18] Ringeval F, Schuller B, Valstar M, Gratch J, Cowie R, Scherer S, et al. “Avec 2017: real-life depression, and affect recognition workshop and challenge,” in Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, AVEC '17 (New York, NY: Association for Computing Machinery), 2017; 3-9.
[19] Salishev S, Barabanov A, Kocharov D, Skrelin P, Moiseev M. “Voice activity detector (vad) based on long-term mel frequency band features,” in Text, Speech, and Dialogue, eds P. Sojka, A. Horák, I. Kopeček, and Pala, K. (Cham: Springer International Publishing), 2016; 352-358.
[20] Eyben F, Scherer KR, Schuller BW, Sundberg J, André E, Busso C, et al. The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing. IEEE Trans. Affect. Comput. 2015; 7: 190-202. doi: 10.1109/TAFFC.2015.2457417
[21] Hagerer G, Pandit V, Eyben F, Schuller B. “Enhancing lstm rnn-based speech overlap detection by artificially mixed data,” in Audio Engineering Society Conference: 2017 AES International Conference on Semantic Audio, Erlangen. 2017.
[22] Eyben F, Wöllmer M, Schuller B. “Opensmile: the munich versatile and fast open-source audio feature extractor,” in MM '10 (New York, NY: Association for Computing Machinery), 2010; 1459-1462.
[23] Stappen L, Baird A, Rizos G, Tzirakis P, Du X, Hafner F, et al. “Muse 2020 challenge and workshop: Multimodal sentiment analysis, emotion-target engagement and trustworthiness detection in real-life media: Emotional car reviews in-the-wild,” in Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-Life Media Challenge and Workshop MuSe'20, 2020; 35-44.
[24] Van Rossum G, Drake FL. Python 3 Reference Manual. (Scotts Valley, CA: CreateSpace). 2009.
[25] Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: Large-scale Machine Learning on Heterogeneous Systems. Available online at: https://www.tensorflow.org/ (accessed December, 2015; 13: 2021).
[26] Lin L. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989; 45: 255-268. doi: 10.2307/2532051
