Accurate speech emotion recognition is essential for developing human-facing systems. Recent advancements have included finetuning large, pretrained transformer models like Wav2Vec 2.0. However, the finetuning process requires substantial computational resources, including high-memory GPUs and significant processing time. As the demand for accurate emotion recognition continues to grow, efficient finetuning approaches are needed to reduce the computational burden. Our study focuses on dimensional emotion recognition, predicting attributes such as activation (calm to excited) and valence (negative to positive). We present various finetuning techniques, including full finetuning, partial finetuning of transformer layers, finetuning with mixed precision, partial finetuning with caching, and low-rank adaptation (LoRA) on the Wav2Vec 2.0 base model. We find that partial finetuning with mixed precision achieves performance comparable to full finetuning while increasing training speed by 67%. Caching intermediate representations further boosts efficiency, yielding an 88% speedup and a 71% reduction in learnable parameters. We recommend finetuning the final three transformer layers in mixed precision to balance performance and training efficiency, and adding intermediate representation caching for optimal speed with minimal performance trade-offs. These findings lower the barriers to finetuning speech emotion recognition systems, making accurate emotion recognition more accessible to a broader range of researchers and practitioners.

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.

Many machine learning tasks -- particularly those in affective computing -- are inherently subjective. When asked to classify facial expressions or to rate an individual's attractiveness, humans may disagree with one another, and no single answer may be objectively correct. However, machine learning datasets commonly have just one "ground truth" label for each sample, so models trained on these labels may not perform well on tasks that are subjective in nature. Though allowing models to learn from the individual annotators' ratings may help, most datasets do not provide annotator-specific labels for each sample. To address this issue, we propose SeedBERT, a method for recovering annotator rating distributions from a single label by inducing pre-trained models to attend to different portions of the input. Our human evaluations indicate that SeedBERT's attention mechanism is consistent with human sources of annotator disagreement. Moreover, in our empirical evaluations using large language models, SeedBERT demonstrates substantial gains in performance on downstream subjective tasks compared both to standard deep learning models and to other current models that account explicitly for annotator disagreement.