Abstract We present a tool for the compositional verification and correct-by-construction synthesis of stochastic world models —heterogeneous networks of interdependent stochastic models including discrete and continuous-time Markov chains, Markov decision processes (MDPs), partially observable MDPs, and stochastic multi-player games. Through its unique integration of multiple probabilistic and parametric model checking paradigms, our tool unifies the modelling, verification and synthesis of systems characterised by a combination of probabilistic and nondeterministic uncertainty, discrete and continuous-time behaviour, partial observability, and multi-agent interaction.

Deep learning methods for Automatic Speech Recognition (ASR) often rely on large-scale training datasets, which are typically unavailable in low-resource environments (LREs). This lack of sufficient and representative training data poses a significant challenge for applying ASR systems in specific domains categorized as LREs. In this paper, we provide a comprehensive overview and empirical analysis of state-of-the-art deep learning techniques for ASR, which are primarily designed for high-resource environments (HREs). Our aim is to explore their potential effectiveness in LRE settings. We focus on identifying key factors that influence the adaptation of HRE models to LRE tasks. To this end, we survey advanced deep learning models and conduct a comparative evaluation of their performance in LRE contexts. Additionally, we propose that pre-training ASR models on HRE datasets, followed by domain-specific fine-tuning on LRE data, can significantly enhance performance in data-scarce settings. Using LibriSpeech and WSJ as our HRE datasets, we evaluate these models on two LRE datasets: UASpeech for dysarthria speech and iCUBE, our novel human–robot interaction dataset. Our systematic experiments, involving varying dataset sizes for pre-training, demonstrate the efficacy of combining pre-training and fine-tuning strategies to improve recognition accuracy in LREs.

With advances in deep learning methodologies, Automatic Speech Recognition (ASR) systems have seen impressive results. However, ASR in Low-Resource Environments (LREs) are challenged by a lack of training data for the specific target domain. We propose that data sampling criteria for choosing more informative speech samples can be critical to addressing the problem of training data bottleneck. Our proposed Local Aggregation BERT (LABERT) method for self-supervised speech representation learning fuses an active learning model with an adapted local aggregation metric. Active learning is used to pick informative speech units, whereas the aggregation metric forces the model to move similar data together in the latent space while separating dissimilar instances to detect hidden units in LRE tasks. We evaluate LABERT with two LRE datasets: I-CUBE and UASpeech to explore the performance of our model in the LRE ASR problems.

Recent improvements in Automatic Speech Recognition (ASR) systems obtain extraordinary results. However, there are specific domains where training data can be either limited or not representative enough, which are known as Low-Resource Environments (LRE). In this paper, we present ScoutWav, a network that integrates context-based word boundaries with self-supervised learning, wav2vec 2.0, to present a low-resource ASR model. First, we pre-train a model on High-Resource Environment (HRE) datasets and then fine-tune with the LRE datasets to obtain context-based word boundaries. The resulting word boundaries are used for fine-tuning with a pre-trained and iteratively refined wav2vec 2.0 to learn appropriate representations for the downstream ASR task. Our refinement strategy for wav2vec 2.0 comes determined by using canonical correlation analysis (CCA) to detect which layers need updating. This dynamic refinement allows wav2vec 2.0 to learn more descriptive LRE-based representations. Finally, the learned representations in the two-step fine-tuned wav2vec 2.0 framework are fed back to the Scout Network for the downstream task. We carried out experiments with two different LRE datasets: I-CUBE and UASpeech. Our experiments demonstrate that using the target domain word boundary after pre-training and automatic layer analysis, ScoutWav shows up to 12% relative WER reduction on the LR data.