Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-encoders
Objective speech disorder classification for speakers with communication difficulty is desirable for diagnosis and administering therapy. With the current state of speech technology, it is evident to propose neural networks for this application. But neural network model training is hampered by a lack of labeled disordered speech data. In this research, we apply an extended version of Factorized Hierarchical Variational Auto-encoders (FHVAE) for representation learning on disordered speech. The FHVAE model extracts both content-related and sequence-related latent variables from speech data, and we utilize the extracted variables to explore how disorder type information is represented in the latent variables. For better classification performance, the latent variables are aggregated at the word and sentence level. We show that an extension of the FHVAE model succeeds in the better disentanglement of the content-related and sequence-related related representations, but both representations are still required for best results on disorder type classification.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationDisentanglementRepresentation LearningSentenceSimilar Papers 제목 키워드 기반
A Semantic Information-based Hierarchical Speech Enhancement Method Using Factorized Codec and Diffusion Model
Most current speech enhancement (SE) methods recover clean speech from noisy inputs by directly estimating time-frequency masks or spectrums. However, these approaches often neglect the distinct attributes, such as seman…
Speech EnhancementToward Real-World Voice Disorder Classification
Objective: Voice disorders significantly compromise individuals' ability to speak in their daily lives. Without early diagnosis and treatment, these disorders may deteriorate drastically. Thus, automatic classification s…
ClassificationModel CompressionUnsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of infor…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Speaker Verificationspeech-recognition+1Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders
The electroencephalogram (EEG) is a powerful method to understand how the brain processes speech. Linear models have recently been replaced for this purpose with deep neural networks and yield promising results. In relat…
ClassificationEEGElectroencephalogram (EEG)Multimodal LLMs are not all you need for Pediatric Speech Language Pathology
Speech Sound Disorders (SSD) affect roughly five percent of children, yet speech-language pathologists face severe staffing shortages and unmanageable caseloads. We test a hierarchical approach to SSD classification on t…
Binary ClassificationSpeech RecognitionData Augmentation