Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms

Multimodal schizophrenia assessment systems have gained traction over the last few years. This work introduces a schizophrenia assessment system to discern between prominent symptom classes of schizophrenia and predict an overall schizophrenia severity score. We develop a Vector Quantized Variational Auto-Encoder (VQ-VAE) based Multimodal Representation Learning (MRL) model to produce task-agnostic speech representations from vocal Tract Variables (TVs) and Facial Action Units (FAUs). These representations are then used in a Multi-Task Learning (MTL) based downstream prediction model to obtain class labels and an overall severity score. The proposed framework outperforms the previous works on the multi-class classification task across all evaluation metrics (Weighted F1 score, AUC-ROC score, and Weighted Accuracy). Additionally, it estimates the schizophrenia severity score, a task not addressed by earlier approaches.

A Multimodal Framework for the Assessment of the Schizophrenia Spectrum

This paper presents a novel multimodal framework to distinguish between different symptom classes of subjects in the schizophrenia spectrum and healthy controls using audio, video, and text modalities. We implemented Convolution Neural Network and Long Short Term Memory based unimodal models and experimented on various multimodal fusion approaches to come up with the proposed framework. We utilized a minimal Gated multimodal unit (mGMU) to obtain a bi-modal intermediate fusion of the features extracted from the input modalities before finally fusing the outputs of the bimodal fusions to perform subject-wise classifications. The use of mGMU units in the multimodal framework improved the performance in both weighted f1-score and weighted AUC-ROC scores.

Accent Conversion with Articulatory Representations

Conversion of non-native accented speech to native (American) English has a wide range of applications such as improving intelligibility of non-native speech. Previous work on this domain has used phonetic posteriograms as the target speech representation to train an acoustic model which is then used to extract a compact representation of input speech for accent conversion. In this work, we introduce the idea of using an effective articulatory speech representation, extracted from an acoustic-to-articulatory speech inversion system, to improve the acoustic model used in accent conversion. The idea to incorporate articulatory representations originates from their ability to well characterize accents in speech. To incorporate articulatory representations with conventional phonetic posteriograms, a multi-task learning based acoustic model is proposed. Objective and subjective evaluations show that the use of articulatory representations can improve the effectiveness of accent conversion.

Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings

Creating Automatic Speech Recognition (ASR) systems that are robust and resilient to classroom conditions is paramount to the development of AI tools to aid teachers and students. In this work, we study the efficacy of continued pretraining (CPT) in adapting Wav2vec2.0 to the classroom domain. We show that CPT is a powerful tool in that regard and reduces the Word Error Rate (WER) of Wav2vec2.0-based models by upwards of 10%. More specifically, CPT improves the model's robustness to different noises, microphones, classroom conditions as well as classroom demographics. Our CPT models show improved ability to generalize to different demographics unseen in the labeled finetuning data.

A multi-modal approach for identifying schizophrenia using cross-modal attention

This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms. We developed a multi-modal schizophrenia classification system using audio, video, and text. Facial action units and vocal tract variables were extracted as low-level features from video and audio respectively, which were then used to compute high-level coordination features that served as the inputs to the audio and video modalities. Context-independent text embeddings extracted from transcriptions of speech were used as the input for the text modality. The multi-modal system is developed by fusing a segment-to-session-level classifier for video and audio modalities with a text model based on a Hierarchical Attention Network (HAN) with cross-modal attention. The proposed multi-modal system outperforms the previous state-of-the-art multi-modal system by 8.53% in the weighted average F1 score.

Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults

Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance given sufficient data. However, this progress doesn't readily extend to ASR for children due to the limited availability of suitable child-specific databases and the distinct characteristics of children's speech. A recent study investigated leveraging the My Science Tutor (MyST) children's speech corpus to enhance Whisper's performance in recognizing children's speech. They were able to demonstrate some improvement on a limited testset. This paper builds on these findings by enhancing the utility of the MyST dataset through more efficient data preprocessing. We reduce the Word Error Rate (WER) on the MyST testset 13.93% to 9.11% with Whisper-Small and from 13.23% to 8.61% with Whisper-Medium and show that this improvement can be generalized to unseen datasets. We also highlight important challenges towards improving children's ASR performance. The results showcase the viable and efficient integration of Whisper for effective children's speech recognition.

Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables

The performance of deep learning models depends significantly on their capacity to encode input features efficiently and decode them into meaningful outputs. Better input and output representation has the potential to boost models' performance and generalization. In the context of acoustic-to-articulatory speech inversion (SI) systems, we study the impact of utilizing speech representations acquired via self-supervised learning (SSL) models, such as HuBERT compared to conventional acoustic features. Additionally, we investigate the incorporation of novel tract variables (TVs) through an improved geometric transformation model. By combining these two approaches, we improve the Pearson product-moment correlation (PPMC) scores which evaluate the accuracy of TV estimation of the SI system from 0.7452 to 0.8141, a 6.9% increase. Our findings underscore the profound influence of rich feature representations from SSL models and improved geometric transformations with target TVs on the enhanced functionality of SI systems.

Speaker-independent Speech Inversion for Estimation of Nasalance

The velopharyngeal (VP) valve regulates the opening between the nasal and oral cavities. This valve opens and closes through a coordinated motion of the velum and pharyngeal walls. Nasalance is an objective measure derived from the oral and nasal acoustic signals that correlate with nasality. In this work, we evaluate the degree to which the nasalance measure reflects fine-grained patterns of VP movement by comparison with simultaneously collected direct measures of VP opening using high-speed nasopharyngoscopy (HSN). We show that nasalance is significantly correlated with the HSN signal, and that both match expected patterns of nasality. We then train a temporal convolution-based speech inversion system in a speaker-independent fashion to estimate VP movement for nasality, using nasalance as the ground truth. In further experiments, we also show the importance of incorporating source features (from glottal activity) to improve nasality prediction.

Acoustic-to-Articulatory Speech Inversion Features for Mispronunciation Detection of /r/ in Child Speech Sound Disorders

Acoustic-to-articulatory speech inversion could enhance automated clinical mispronunciation detection to provide detailed articulatory feedback unattainable by formant-based mispronunciation detection algorithms; however, it is unclear the extent to which a speech inversion system trained on adult speech performs in the context of (1) child and (2) clinical speech. In the absence of an articulatory dataset in children with rhotic speech sound disorders, we show that classifiers trained on tract variables from acoustic-to-articulatory speech inversion meet or exceed the performance of state-of-the-art features when predicting clinician judgment of rhoticity. Index Terms: rhotic, speech sound disorder, mispronunciation detection

The Secret Source : Incorporating Source Features to Improve Acoustic-to-Articulatory Speech Inversion

In this work, we incorporated acoustically derived source features, aperiodicity, periodicity and pitch as additional targets to an acoustic-to-articulatory speech inversion (SI) system. We also propose a Temporal Convolution based SI system, which uses auditory spectrograms as the input speech representation, to learn long-range dependencies and complex interactions between the source and vocal tract, to improve the SI task. The experiments are conducted with both the Wisconsin X-ray microbeam (XRMB) and Haskins Production Rate Comparison (HPRC) datasets, with comparisons done with respect to three baseline SI model architectures. The proposed SI system with the HPRC dataset gains an improvement of close to 28% when the source features are used as additional targets. The same SI system outperforms the current best performing SI models by around 9% on the XRMB dataset.