Beyond Binary: Multiclass Paraphasia Detection with Generative Pretrained Transformers and End-to-End Models

Aphasia is a language disorder that can lead to speech errors known as paraphasias, which involve the misuse, substitution, or invention of words. Automatic paraphasia detection can help those with Aphasia by facilitating clinical assessment and treatment planning options. However, most automatic paraphasia detection works have focused solely on binary detection, which involves recognizing only the presence or absence of a paraphasia. Multiclass paraphasia detection represents an unexplored area of research that focuses on identifying multiple types of paraphasias and where they occur in a given speech segment. We present novel approaches that use a generative pretrained transformer (GPT) to identify paraphasias from transcripts as well as two end-to-end approaches that focus on modeling both automatic speech recognition (ASR) and paraphasia classification as multiple sequences vs. a single sequence. We demonstrate that a single sequence model outperforms GPT baselines for multiclass paraphasia detection.

Seq2seq for Automatic Paraphasia Detection in Aphasic Speech

Paraphasias are speech errors that are often characteristic of aphasia and they represent an important signal in assessing disease severity and subtype. Traditionally, clinicians manually identify paraphasias by transcribing and analyzing speech-language samples, which can be a time-consuming and burdensome process. Identifying paraphasias automatically can greatly help clinicians with the transcription process and ultimately facilitate more efficient and consistent aphasia assessment. Previous research has demonstrated the feasibility of automatic paraphasia detection by training an automatic speech recognition (ASR) model to extract transcripts and then training a separate paraphasia detection model on a set of hand-engineered features. In this paper, we propose a novel, sequence-to-sequence (seq2seq) model that is trained end-to-end (E2E) to perform both ASR and paraphasia detection tasks. We show that the proposed model outperforms the previous state-of-the-art approach for both word-level and utterance-level paraphasia detection tasks and provide additional follow-up evaluations to further understand the proposed model behavior.

Automatic Disfluency Detection from Untranscribed Speech

Speech disfluencies, such as filled pauses or repetitions, are disruptions in the typical flow of speech. Stuttering is a speech disorder characterized by a high rate of disfluencies, but all individuals speak with some disfluencies and the rates of disfluencies may by increased by factors such as cognitive load. Clinically, automatic disfluency detection may help in treatment planning for individuals who stutter. Outside of the clinic, automatic disfluency detection may serve as a pre-processing step to improve natural language understanding in downstream applications. With this wide range of applications in mind, we investigate language, acoustic, and multimodal methods for frame-level automatic disfluency detection and categorization. Each of these methods relies on audio as an input. First, we evaluate several automatic speech recognition (ASR) systems in terms of their ability to transcribe disfluencies, measured using disfluency error rates. We then use these ASR transcripts as input to a language-based disfluency detection model. We find that disfluency detection performance is largely limited by the quality of transcripts and alignments. We find that an acoustic-based approach that does not require transcription as an intermediate step outperforms the ASR language approach. Finally, we present multimodal architectures which we find improve disfluency detection performance over the unimodal approaches. Ultimately, this work introduces novel approaches for automatic frame-level disfluency and categorization. In the long term, this will help researchers incorporate automatic disfluency detection into a range of applications.

Articulatory Coordination for Speech Motor Tracking in Huntington Disease

Huntington Disease (HD) is a progressive disorder which often manifests in motor impairment. Motor severity (captured via motor score) is a key component in assessing overall HD severity. However, motor score evaluation involves in-clinic visits with a trained medical professional, which are expensive and not always accessible. Speech analysis provides an attractive avenue for tracking HD severity because speech is easy to collect remotely and provides insight into motor changes. HD speech is typically characterized as having irregular articulation. With this in mind, acoustic features that can capture vocal tract movement and articulatory coordination are particularly promising for characterizing motor symptom progression in HD. In this paper, we present an experiment that uses Vocal Tract Coordination (VTC) features extracted from read speech to estimate a motor score. When using an elastic-net regression model, we find that VTC features significantly outperform other acoustic features across varied-length audio segments, which highlights the effectiveness of these features for both short- and long-form reading tasks. Lastly, we analyze the F-value scores of VTC features to visualize which channels are most related to motor score. This work enables future research efforts to consider VTC features for acoustic analyses which target HD motor symptomatology tracking.

Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network

In recent years, deep-learning-based speech emotion recognition models have outperformed classical machine learning models. Previously, neural network designs, such as Multitask Learning, have accounted for variations in emotional expressions due to demographic and contextual factors. However, existing models face a few constraints: 1) they rely on a clear definition of domains (e.g. gender, noise condition, etc.) and the availability of domain labels; 2) they often attempt to learn domain-invariant features while emotion expressions can be domain-specific. In the present study, we propose the Nonparametric Hierarchical Neural Network (NHNN), a lightweight hierarchical neural network model based on Bayesian nonparametric clustering. In comparison to Multitask Learning approaches, the proposed model does not require domain/task labels. In our experiments, the NHNN models generally outperform the models with similar levels of complexity and state-of-the-art models in within-corpus and cross-corpus tests. Through clustering analysis, we show that the NHNN models are able to learn group-specific features and bridge the performance gap between groups.

Best Practices for Noise-Based Augmentation to Improve the Performance of Emotion Recognition "In the Wild"

Emotion recognition as a key component of high-stake downstream applications has been shown to be effective, such as classroom engagement or mental health assessments. These systems are generally trained on small datasets collected in single laboratory environments, and hence falter when tested on data that has different noise characteristics. Multiple noise-based data augmentation approaches have been proposed to counteract this challenge in other speech domains. But, unlike speech recognition and speaker verification, in emotion recognition, noise-based data augmentation may change the underlying label of the original emotional sample. In this work, we generate realistic noisy samples of a well known emotion dataset (IEMOCAP) using multiple categories of environmental and synthetic noise. We evaluate how both human and machine emotion perception changes when noise is introduced. We find that some commonly used augmentation techniques for emotion recognition significantly change human perception, which may lead to unreliable evaluation metrics such as evaluating efficiency of adversarial attack. We also find that the trained state-of-the-art emotion recognition models fail to classify unseen noise-augmented samples, even when trained on noise augmented datasets. This finding demonstrates the brittleness of these systems in real-world conditions. We propose a set of recommendations for noise-based augmentation of emotion datasets and for how to deploy these emotion recognition systems "in the wild".

Why Should I Trust a Model is Private? Using Shifts in Model Explanation for Evaluating Privacy-Preserving Emotion Recognition Model

Privacy preservation is a crucial component of any real-world application. Yet, in applications relying on machine learning backends, this is challenging because models often capture more than a designer may have envisioned, resulting in the potential leakage of sensitive information. For example, emotion recognition models are susceptible to learning patterns between the target variable and other sensitive variables, patterns that can be maliciously re-purposed to obtain protected information. In this paper, we concentrate on using interpretable methods to evaluate a model's efficacy to preserve privacy with respect to sensitive variables. We focus on saliency-based explanations, explanations that highlight regions of the input text, which allows us to understand how model explanations shift when models are trained to preserve privacy. We show how certain commonly-used methods that seek to preserve privacy might not align with human perception of privacy preservation. We also show how some of these induce spurious correlations in the model between the input and the primary as well as secondary task, even if the improvement in evaluation metric is significant. Such correlations can hence lead to false assurances about the perceived privacy of the model because especially when used in cross corpus conditions. We conduct crowdsourcing experiments to evaluate the inclination of the evaluators to choose a particular model for a given task when model explanations are provided, and find that correlation of interpretation differences with sociolinguistic biases can be used as a proxy for user trust.

Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training

Robustness to environmental noise is important to creating automatic speech emotion recognition systems that are deployable in the real world. Prior work on noise robustness has assumed that systems would not make use of sample-by-sample training noise conditions, or that they would have access to unlabelled testing data to generalize across noise conditions. We avoid these assumptions and introduce the resulting task as heterogeneous condition training. We show that with full knowledge of the test noise conditions, we can improve performance by dynamically routing samples to specialized feature encoders for each noise condition, and with partial knowledge, we can use known noise conditions and domain adaptation algorithms to train systems that generalize well to unseen noise conditions. We then extend these improvements to the multimodal setting by dynamically routing samples to maintain temporal ordering, resulting in significant improvements over approaches that do not specialize or generalize based on noise type.

Quantifying the Effects of COVID-19 on Mental Health Support Forums

The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health. Understanding its impact can inform strategies for mitigating negative consequences. In this work, we seek to better understand the effects of COVID-19 on mental health by examining discussions within mental health support communities on Reddit. First, we quantify the rate at which COVID-19 is discussed in each community, or subreddit, in order to understand levels of preoccupation with the pandemic. Next, we examine the volume of activity in order to determine whether the quantity of people seeking online mental health support has risen. Finally, we analyze how COVID-19 has influenced language use and topics of discussion within each subreddit.

Classification of Huntington Disease using Acoustic and Lexical Features

Speech is a critical biomarker for Huntington Disease (HD), with changes in speech increasing in severity as the disease progresses. Speech analyses are currently conducted using either transcriptions created manually by trained professionals or using global rating scales. Manual transcription is both expensive and time-consuming and global rating scales may lack sufficient sensitivity and fidelity. Ultimately, what is needed is an unobtrusive measure that can cheaply and continuously track disease progression. We present first steps towards the development of such a system, demonstrating the ability to automatically differentiate between healthy controls and individuals with HD using speech cues. The results provide evidence that objective analyses can be used to support clinical diagnoses, moving towards the tracking of symptomatology outside of laboratory and clinical environments.