Discrete tokens provide compact and domain-adaptable representations of speech features. However, their application to disordered speech, characterized by articulation imprecision and significant mismatch with normal voice, remains unexplored. To this end, this paper proposes novel phone-purity guided (PPG) discrete tokens to address the weakened phonetic discrimination arising during unsupervised K-means clustering or vector quantization of continuous features. Phonetic label supervision is incorporated to regularize the maximum likelihood and reconstruction error costs in standard K-means and VAE-VQ-based token extraction. Experiments on the UASpeech corpus show that PPG-based discrete tokens extracted from HuBERT consistently outperform hybrid TDNN and End-to-End (E2E) Conformer systems using non-PPG tokens. Statistically significant word error rate (WER) reductions of up to 0.99% and 1.77% absolute (3.21% and 4.82% relative) are achieved across varying codebook sizes for the 16 UASpeech test dysarthric speakers. The lowest WER of 23.25% is obtained by combining systems using complementary token features. Consistent improvements are also observed in phone purity, and t-SNE visualizations demonstrate sharper decision boundaries between K-means/VAE-VQ clusters with the introduction of phone-purity guidance.

This paper presents a novel mixed-precision quantization approach for speech foundation models that tightly integrates mixed-precision learning and quantized model parameter estimation into one single model compression stage. Experiments conducted on LibriSpeech dataset with fine-tuned wav2vec2.0-base and HuBERT-large models suggest the resulting mixed-precision quantized models increased the lossless compression ratio by factors up to 1.7x and 1.9x over the respective uniform-precision and two-stage mixed-precision quantized baselines that perform precision learning and model parameters quantization in separate and disjointed stages, while incurring no statistically word error rate (WER) increase over the 32-bit full-precision models. The system compression time of wav2vec2.0-base and HuBERT-large models is reduced by up to 1.9 and 1.5 times over the two-stage mixed-precision baselines, while both produce lower WERs. The best-performing 3.5-bit mixed-precision quantized HuBERT-large model produces a lossless compression ratio of 8.6x over the 32-bit full-precision system.

Automatic recognition of dysarthric speech remains a highly challenging task to date. Neuro-motor conditions and co-occurring physical disabilities create difficulty in large-scale data collection for ASR system development. Adapting SSL pre-trained ASR models to limited dysarthric speech via data-intensive parameter fine-tuning leads to poor generalization. To this end, this paper presents an extensive comparative study of various data augmentation approaches to improve the robustness of pre-trained ASR model fine-tuning to dysarthric speech. These include: a) conventional speaker-independent perturbation of impaired speech; b) speaker-dependent speed perturbation, or GAN-based adversarial perturbation of normal, control speech based on their time alignment against parallel dysarthric speech; c) novel Spectral basis GAN-based adversarial data augmentation operating on non-parallel data. Experiments conducted on the UASpeech corpus suggest GAN-based data augmentation consistently outperforms fine-tuned Wav2vec2.0 and HuBERT models using no data augmentation and speed perturbation across different data expansion operating points by statistically significant word error rate (WER) reductions up to 2.01% and 0.96% absolute (9.03% and 4.63% relative) respectively on the UASpeech test set of 16 dysarthric speakers. After cross-system outputs rescoring, the best system produced the lowest published WER of 16.53% (46.47% on very low intelligibility) on UASpeech.

We proposed a new framework to measure the similarity of topological structure between complex trajectories. There are three steps in the framework. A complex trajectory is first represented by a graph structure which consists of nodes and edges. Secondly, we developed a Comprehensive Structure Matching (CSM) algorithm to identify all common structures between the complex trajectories of interest. Thirdly, we used the Jaccard similarity coefficient to evaluate the similarity between two complex trajectories. We used synthetic graph data to evaluate the CSM method and examine its performance by comparing against that of the VF2 and the exact graph edit distance (EGED) algorithms. Results show that the CSM algorithm outperforms the EGED in terms of the computation efficiency. The CSM is more comprehensive than the VF2 algorithm as it further considers the partial isomorphism. We used the CSM algorithm to examine the 1993 to 2012 complex trajectories of anticyclonic eddies in the South China Sea (SCS). The CSM successfully found the complex trajectories that are similar to a thoroughly-studied ACE3 trajectory in the SCS. From the similar trajectories, we identified a dominant migrating path of the ocean eddies in the northern SCS. The CSM also successfully identified some new complex trajectories that propagated across the 18°N parallel in the SCS, which were not reported before. It also further identified multiple common structure models of the complex trajectories. These findings help us better understand the behaviors and the evolution of the mesoscale eddies in the SCS.