Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings

Speech processing techniques are useful for analyzing speech and language development in children with Autism Spectrum Disorder (ASD), who are often varied and delayed in acquiring these skills. Early identification and intervention are crucial, but traditional assessment methodologies such as caregiver reports are not adequate for the requisite behavioral phenotyping. Natural Language Sample (NLS) analysis has gained attention as a promising complement. Researchers have developed benchmarks for spoken language capabilities in children with ASD, obtainable through the analysis of NLS. This paper proposes applications of speech processing technologies in support of automated assessment of children's spoken language development by classification between child and adult speech and between speech and nonverbal vocalization in NLS, with respective F1 macro scores of 82.6% and 67.8%, underscoring the potential for accurate and scalable tools for ASD research and clinical use.Comment: Accepted to Interspeech 2023, 5 page

Audio-visual child-adult speaker classification in dyadic interactions

Interactions involving children span a wide range of important domains from learning to clinical diagnostic and therapeutic contexts. Automated analyses of such interactions are motivated by the need to seek accurate insights and offer scale and robustness across diverse and wide-ranging conditions. Identifying the speech segments belonging to the child is a critical step in such modeling. Conventional child-adult speaker classification typically relies on audio modeling approaches, overlooking visual signals that convey speech articulation information, such as lip motion. Building on the foundation of an audio-only child-adult speaker classification pipeline, we propose incorporating visual cues through active speaker detection and visual processing models. Our framework involves video pre-processing, utterance-level child-adult speaker detection, and late fusion of modality-specific predictions. We demonstrate from extensive experiments that a visually aided classification pipeline enhances the accuracy and robustness of the classification. We show relative improvements of 2.38% and 3.97% in F1 macro score when one face and two faces are visible, respectively.Comment: In review for ICASSP 2024, 5 page

Meta-learning with Latent Space Clustering in Generative Adversarial Network for Speaker Diarization

The performance of most speaker diarization systems with x-vector embeddings is both vulnerable to noisy environments and lacks domain robustness. Earlier work on speaker diarization using generative adversarial network (GAN) with an encoder network (ClusterGAN) to project input x-vectors into a latent space has shown promising performance on meeting data. In this paper, we extend the ClusterGAN network to improve diarization robustness and enable rapid generalization across various challenging domains. To this end, we fetch the pre-trained encoder from the ClusterGAN and fine-tune it by using prototypical loss (meta-ClusterGAN or MCGAN) under the meta-learning paradigm. Experiments are conducted on CALLHOME telephonic conversations, AMI meeting data, DIHARD II (dev set) which includes challenging multi-domain corpus, and two child-clinician interaction corpora (ADOS, BOSCC) related to the autism spectrum disorder domain. Extensive analyses of the experimental data are done to investigate the effectiveness of the proposed ClusterGAN and MCGAN embeddings over x-vectors. The results show that the proposed embeddings with normalized maximum eigengap spectral clustering (NME-SC) back-end consistently outperform Kaldi state-of-the-art z-vector diarization system. Finally, we employ embedding fusion with x-vectors to provide further improvement in diarization performance. We achieve a relative diarization error rate (DER) improvement of 6.67% to 53.93% on the aforementioned datasets using the proposed fused embeddings over x-vectors. Besides, the MCGAN embeddings provide better performance in the number of speakers estimation and short speech segment diarization as compared to x-vectors and ClusterGAN in telephonic data.Comment: Submitted to IEEE/ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSIN

Enhancing Child Vocalization Classification in Multi-Channel Child-Adult Conversations Through Wav2vec2 Children ASR Features

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that often emerges in early childhood. ASD assessment typically involves an observation protocol including note-taking and ratings of child's social behavior conducted by a trained clinician. A robust machine learning (ML) model that is capable of labeling adult and child audio has the potential to save significant time and labor in manual coding children's behaviors. This may assist clinicians capture events of interest, better communicate events with parents, and educate new clinicians. In this study, we leverage the self-supervised learning model, Wav2Vec 2.0 (W2V2), pretrained on 4300h of home recordings of children under 5 years old, to build a unified system that performs both speaker diarization (SD) and vocalization classification (VC) tasks. We apply this system to two-channel audio recordings of brief 3-5 minute clinician-child interactions using the Rapid-ABC corpus. We propose a novel technique by introducing auxiliary features extracted from W2V2-based automatic speech recognition (ASR) system for children under 4 years old to improve children's VC task. We test our proposed method of improving children's VC task on two corpora (Rapid-ABC and BabbleCor) and observe consistent improvements. Furthermore, we reach, or perhaps outperform, the state-of-the-art performance of BabbleCor.Comment: Submitted to ICASSP 202

Auditory-motor adaptation is reduced in adults who stutter but not in children who stutter

Previous studies have shown that adults who stutter produce smaller corrective motor responses to compensate for unexpected auditory perturbations in comparison to adults who do not stutter, suggesting that stuttering may be associated with deficits in integration of auditory feedback for online speech monitoring. In this study, we examined whether stuttering is also associated with deficiencies in integrating and using discrepancies between expect ed and received auditory feedback to adaptively update motor programs for accurate speech production. Using a sensorimotor adaptation paradigm, we measured adaptive speech responses to auditory formant frequency perturbations in adults and children who stutter and their matched nonstuttering controls. We found that the magnitude of the speech adaptive response for children who stutter did not differ from that of fluent children. However, the adaptation magnitude of adults who stutter in response to formant perturbation was significantly smaller than the adaptation magnitude of adults who do not stutter. Together these results indicate that stuttering is associated with deficits in integrating discrepancies between predicted and received auditory feedback to calibrate the speech production system in adults but not children. This auditory-motor integration deficit thus appears to be a compensatory effect that develops over years of stuttering

Attachment in adults with high-functioning autism

This study assessed attachment security in adults with high-functioning autism spectrum disorders, using the Adult Attachment Interview (AAI; George, Kaplan & Main, 1996). Of twenty participants, three were classified as securely attached, the same proportion as would be expected in a general clinical sample. Participants’ AAIs were less coherent and lower in reflective function than those of controls, who were matched for attachment status and mood disorder. A parallel interview suggested that some aspects of participants’ responses were influenced by their general discourse style, while other AAI scale scores appeared to reflect their state of mind with respect to attachment more specifically. There was little evidence that attachment security was related to IQ, autistic symptomatology or theory of mind. This study suggests that adults with autism can engage with the AAI and produce scoreable narratives of their attachment experiences, and a minority demonstrate secure attachment