Large Language Models for Dysfluency Detection in Stuttered Speech

Accurately detecting dysfluencies in spoken language can help to improve the performance of automatic speech and language processing components and support the development of more inclusive speech and language technologies. Inspired by the recent trend towards the deployment of large language models (LLMs) as universal learners and processors of non-lexical inputs, such as audio and video, we approach the task of multi-label dysfluency detection as a language modeling problem. We present hypotheses candidates generated with an automatic speech recognition system and acoustic representations extracted from an audio encoder model to an LLM, and finetune the system to predict dysfluency labels on three datasets containing English and German stuttered speech. The experimental results show that our system effectively combines acoustic and lexical information and achieves competitive results on the multi-label stuttering detection task.

Classifying Dementia in the Presence of Depression: A Cross-Corpus Study

Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected. Depression has shared symptoms with dementia, adding complexity to diagnoses. The research focus so far has been on binary classification of dementia (DEM) and healthy controls (HC) using speech from picture description tests from a single dataset. In this work, we apply established baseline systems to discriminate cognitive impairment in speech from the semantic Verbal Fluency Test and the Boston Naming Test using text, audio and emotion embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform cross-corpus and mixed-corpus experiments on two independently recorded German datasets to investigate generalization to larger populations and different recording conditions. In a detailed error analysis, we look at depression as a secondary diagnosis to understand what our classifiers actually learn.

A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem

Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, this does not match the nature of stuttering, in which one dysfluency seldom comes alone but rather co-occurs with others. This paper explores multi-language and cross-corpus end-to-end stuttering detection as a multi-label problem using a modified wav2vec 2.0 system with an attention-based classification head and multi-task learning. We evaluate the method using combinations of three datasets containing English and German stuttered speech, one containing speech modified by fluency shaping. The experimental results and an error analysis show that multi-label stuttering detection systems trained on cross-corpus and multi-language data achieve competitive results but performance on samples with multiple labels stays below over-all detection results.

Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem

Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent works have treated stuttering as a multi-class classification problem or viewed detecting each dysfluency type as an isolated task; that does not capture the nature of stuttering, where one dysfluency seldom comes alone, i.e., co-occurs with others. This work explores an approach based on a modified wav2vec 2.0 system for end-to-end stuttering detection and classification as a multi-label problem. The method is evaluated on combinations of three datasets containing English and German stuttered speech, yielding state-of-the-art results for stuttering detection on the SEP-28k-Extended dataset. Experimental results provide evidence for the transferability of features and the generalizability of the method across datasets and languages.

The Importance of Speech Stimuli for Pathologic Speech Classification

Current findings show that pre-trained wav2vec 2.0 models can be successfully used as feature extractors to discriminate on speaker-based tasks. We demonstrate that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be effectively used for binary classification of various types of pathologic speech. We examine the pathologies laryngectomy, oral squamous cell carcinoma, parkinson's disease and cleft lip and palate for this purpose. The results show that a distinction between pathological and healthy voices, especially with latent representations from the lower layers, performs well with the lowest accuracy from 77.2% for parkinson's disease to 100% for laryngectomy classification. However, cross-pathology and cross-healthy tests show that the trained classifiers seem to be biased. The recognition rates vary considerably if there is a mismatch between training and out-of-domain test data, e.g., in age, spoken content or acoustic conditions.

Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?

The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representing healthy speech. In this work, we train neural networks, large margin classifiers, and tree boosting machines to distinguish between four different pathologies: Parkinson's disease, laryngeal cancer, cleft lip and palate, and oral squamous cell carcinoma. We demonstrate that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be effectively used to classify these types of pathological voices. We evaluate the robustness of our classifiers by adding room impulse responses to the test data and by applying them to unseen speech corpora. Our approach achieves unweighted average F1-Scores between 74.1% and 96.4%, depending on the model and the noise conditions used. The systems generalize and perform well on unseen data of healthy speakers sampled from a variety of different sources.

Representation Learning Strategies to Model Pathological Speech: Effect of Multiple Spectral Resolutions

This paper considers a representation learning strategy to model speech signals from patients with Parkinson's disease and cleft lip and palate. In particular, it compares different parametrized representation types such as wideband and narrowband spectrograms, and wavelet-based scalograms, with the goal of quantifying the representation capacity of each. Methods for quantification include the ability of the proposed model to classify different pathologies and the associated disease severity. Additionally, this paper proposes a novel fusion strategy called multi-spectral fusion that combines wideband and narrowband spectral resolutions using a representation learning strategy based on autoencoders. The proposed models are able to classify the speech from Parkinson's disease patients with accuracy up to 95\%. The proposed models were also able to asses the dysarthria severity of Parkinson's disease patients with a Spearman correlation up to 0.75. These results outperform those observed in literature where the same problem was addressed with the same corpus.

The Influence of Dataset Partitioning on Dysfluency Detection Systems

This paper empirically investigates the influence of different data splits and splitting strategies on the performance of dysfluency detection systems. For this, we perform experiments using wav2vec 2.0 models with a classification head as well as support vector machines (SVM) in conjunction with the features extracted from the wav2vec 2.0 model to detect dysfluencies. We train and evaluate the systems with different non-speaker-exclusive and speaker-exclusive splits of the Stuttering Events in Podcasts (SEP-28k) dataset to shed some light on the variability of results w.r.t. to the partition method used. Furthermore, we show that the SEP-28k dataset is dominated by only a few speakers, making it difficult to evaluate. To remedy this problem, we created SEP-28k-Extended (SEP-28k-E), containing semi-automatically generated speaker and gender information for the SEP-28k corpus, and suggest different data splits, each useful for evaluating other aspects of methods for dysfluency detection.

Is Speech Pathology a Biomarker in Automatic Speaker Verification?

With the advancements in deep learning (DL) and an increasing interest in data-driven speech processing methods, a major challenge for speech data scientists in the healthcare domain is the anonymization of pathological speech, which is a required step to be able to make them accessible as a public training resource. In this paper, we investigate pathological speech data and compare their speaker verifiability with that of healthy individuals. We utilize a large pathological speech corpus of more than 2,000 test subjects with various speech and voice disorders from different ages and apply DL-based automatic speaker verification (ASV) techniques. As a result, we obtained a mean equal error rate (EER) of 0.86% with a standard deviation of 0.16%, which is a factor of three lower than comparable healthy speech databases. We further perform detailed analyses of external influencing factors on ASV such as age, pathology, recording environment, and utterance length, to explore their respective effect. Our findings indicate that speech pathology is a potential biomarker in ASV. This is potentially of high interest for the anonymization of pathological speech data.

Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0

Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving speech recognition systems for people with such non-typical speech or tracking the effectiveness of speech therapy would require systems that can detect dysfluencies while at the same time being able to detect speech techniques acquired in therapy. This paper shows that fine-tuning wav2vec 2.0 for the classification of stuttering on a sizeable English corpus containing stuttered speech, in conjunction with multi-task learning, boosts the effectiveness of the general-purpose wav2vec 2.0 features for detecting stuttering in speech; both within and across languages. We evaluate our method on Fluencybank and the German therapy-centric Kassel State of Fluency (KSoF) dataset by training Support Vector Machine classifiers using features extracted from the fine-tuned models for six different stuttering-related events types: blocks, prolongations, sound repetitions, word repetitions, interjections, and - specific to therapy - speech modifications. Using embeddings from the fine-tuned models leads to relative classification performance gains up to 27\% w.r.t. F1-score.