Despite the remarkable progress in end-to-end Automatic Speech Recognition (ASR) engines, accurately transcribing dysarthric speech remains a major challenge. In this work, we proposed a two-stage framework for the Speech Accessibility Project Challenge at INTERSPEECH 2025, which combines cutting-edge speech recognition models with LLM-based generative error correction (GER). We assess different configurations of model scales and training strategies, incorporating specific hypothesis selection to improve transcription accuracy. Experiments on the Speech Accessibility Project dataset demonstrate the strength of our approach on structured and spontaneous speech, while highlighting challenges in single-word recognition. Through comprehensive analysis, we provide insights into the complementary roles of acoustic and linguistic modeling in dysarthric speech recognition.

In this paper, we present an encoder-decoder model leveraging Flan-T5 for post-Automatic Speech Recognition (ASR) Generative Speech Error Correction (GenSEC), and we refer to it as FlanEC. We explore its application within the GenSEC framework to enhance ASR outputs by mapping n-best hypotheses into a single output sentence. By utilizing n-best lists from ASR models, we aim to improve the linguistic correctness, accuracy, and grammaticality of final ASR transcriptions. Specifically, we investigate whether scaling the training data and incorporating diverse datasets can lead to significant improvements in post-ASR error correction. We evaluate FlanEC using the HyPoradise dataset, providing a comprehensive analysis of the model’s effectiveness in this domain. Furthermore, we assess the proposed approach under different settings to evaluate model scalability and efficiency, offering valuable insights into the potential of instruction-tuned encoder-decoder models for this task.

The emerging wireless Network-on-Chip (WiNoC) architectures are a viable solution for addressing the scalability limitations of manycore architectures in which multi-hop long-range communications strongly impact both the performance and energy figures of the system. The energy consumption of wired links as well as that of radio communications account for a relevant fraction of the overall energy budget. In this article, we extend the approximate computing paradigm to the case of the on-chip communication system in manycore architectures. We present techniques, circuitries, and programming interfaces aimed at reducing the energy consumption of a WiNoC by exploiting the trade-off energy saving vs. application output degradation. The proposed platform—namely, xWiNoC—uses variable voltage swing links and tunable transmitting power wireless interfaces along with a programming interface that allows the programmer to specify those data structures that are error-resilient. Thus, communications induced by the access to such error-resilient data structures are carried out by using links and radio channels that are configured to work in a low energy mode, albeit by exposing a higher bit error rate. xWiNoC is assessed on a set of applications belonging to different domains in which the trade-off energy vs. performance vs. application result quality is discussed. We found that up to 50% of communication energy saving can be obtained with a negligible impact on the application output quality and 3% in application performance degradation.

Bottleneck features (BNFs) generated with a deep neural network (DNN) have proven to boost spoken language recognition accuracy over basic spectral features significantly. However, BNFs are commonly extracted using language-dependent tied-context phone states as learning targets. Moreover, BNFs are less phonetically expressive than the output layer in a DNN, which is usually not used as a speech feature because of its very high dimensionality hindering further post-processing. In this article, we put forth a novel deep learning framework to overcome all of the above issues and evaluate it on the 2017 NIST Language Recognition Evaluation (LRE) challenge. We use manner and place of articulation as speech attributes, which lead to low-dimensional “universal” phonetic features that can be defined across all spoken languages. To model the asynchronous nature of the speech attributes while capturing their intrinsic relationships in a given speech segment, we introduce a new training scheme for deep architectures based on a Maximal Figure of Merit (MFoM) objective. MFoM introduces non-differentiable metrics into the backpropagation-based approach, which is elegantly solved in the proposed framework. The experimental evidence collected on the recent NIST LRE 2017 challenge demonstrates the effectiveness of our solution. In fact, the performance of speech language recognition (SLR) systems based on spectral features is improved for more than 5% absolute Cavg. Finally, the F1 metric can be brought from 77.6% up to 78.1% by combining the conventional baseline phonetic BNFs with the proposed articulatory attribute features.

Model adaptation is a key technique that enables a modern automatic speech recognition (ASR) system to adjust its parameters, using a small amount of enrolment data, to the nuances in the speech spectrum due to microphone mismatch in the training and test data. In this brief, we investigate four different adaptation schemes for connectionist (also known as hybrid) ASR systems that learn microphone-specific hidden unit contributions, given some adaptation material. This solution is made possible adopting one of the following schemes: 1) the use of Hermite activation functions; 2) the introduction of bias and slope parameters in the sigmoid activation functions; 3) the injection of an amplitude parameter specific for each sigmoid unit; or 4) the combination of 2) and 3). Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space, a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment. Experimental results indicate that the investigated approaches reduce word error rates on the standard Spoke 6 task of the Wall Street Journal corpus compared with unadapted ASR systems. Moreover, the proposed adaptation schemes all perform better than simple multicondition training and comparable favorably against conventional linear regression-based approaches while using up to 15 orders of magnitude fewer parameters. The proposed adaptation strategies are also effective when a single adaptation sentence is available.

We have recently proposed a universal acoustic characterisation to foreign accent recognition, in which any spoken foreign accent was described in terms of a common set of fundamental speech attributes. Although experimental evidence demonstrated the feasibility of our approach, we belive that speech attributes, namely manner and place of articulation, can be better modelled by a deep neural network. In this work, we propose the use of deep neural network trained on telephone bandwidth material from different languages to improve the proposed universal acoustic characterisation. We demonstrate that deeper neural architectures enhance the attribute classification accuracy. Furthermore, we show that improvements in attribute classification carry over to foreign accent recognition by producing a 21% relative improvement over previous baseline on spoken Finnish, and a 5.8% relative improvement on spoken English

This paper shows an innovative Wireless Sensor Network (WSN) architecture based on a flexible Quality of Service (QoS) approach for road traffic management. The aim of this work is to analyse an algorithm that dynamically enables/disables some cameras according to the real need to monitor a given area, It is based on traffic volume measured values. This approach has been developed in order to manage both network topology and workload conditions, using a fuzzy logic controller for a flexible QoS management. Performances of this approach have been evaluated using True Time and Simulink/Matlab.

Advantages provided by the use of wireless technologies in industrial scenarios, like reduced costs of installation, better flexibility, the ability to easily realize temporary deployments (e.g. for monitoring purposes), etc. are discussed by researchers in literature. In these contexts, time-critical transmissions have to be supported by automation networks. In IEEE 802.11 standard protocol, one of most popular technologies for process control networks, Access Points (APs) are responsible for nodes connection. Timeliness of communications can be compromised by transmission of wireless nodes through overloaded APs. This paper shows a new dynamic Load Balancing approach that distributes network load in order to ensure, for each station, best possible performances in terms of soft real-time constraints, in an industrial process control scenario.