Intent Prediction Based On Contextual Factors For Better Automatic Speech Recognition

Automatic speech recognition (ASR) machine learning models are used to recognize spoken commands or queries from users. End-to-end ASR models, which directly map a sequence of input acoustic features into a sequence of words, greatly simplify ASR system building and maintenance. This disclosure describes techniques to improve the performance of end-to-end ASR models by providing predicted user intents as additional inputs. Intent prediction vectors or intent embedding is generated based on user-permitted contextual features using a trained intent prediction network (IPN). The IPN can be trained independently from the ASR model or jointly with the ASR model. Training of the IPN can be performed based on training data that includes user-permitted contextual features, even when such data does not include speech data. The IPN can be retrained when the available contextual feature set changes

Dialog act guided contextual adapter for personalized speech recognition

Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however, does not incorporate an important cue, the dialog act, which is available in a multi-turn dialog scenario. In this work, we propose a dialog act guided contextual adapter network. Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both -- the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing. On industrial voice assistant datasets, our model outperforms both the baselines - dialog act encoder-only model, and the contextual adaptation, leading to the most improvement over the no-context model: 58% average relative word error rate reduction (WERR) in the multi-turn dialog scenario, in comparison to the prior-art contextual adapter, which has achieved 39% WERR over the no-context model.Comment: Accepted at ICASSP 202

Energy management engineering : a predictive energy management system incorporating an adaptive neural network for the direct heating of domestic and industrial fluid mediums.

The objective of this research project is to improve the control and provide a more cost-efficient operation in the direct heating of stored domestic or industrial fluid mediums; such to be achieved by means of an intelligent automated energy management system. For the residential customer this system concept applies to the hot water supply as stored in the familiar hot water cylinder; for the industrial or commercial customer the scope is considerably greater with larger quantities and varieties of fluid mediums. Both areas can obtain significant financial savings with improved energy management. Both consumers and power supply and distribution companies will benefit with increased utilisation of cheaper 'off-peak' electricity; reducing costs and spreading the system load demand. The project has focussed on domestic energy management with a definite view to the wider field of industrial applications. Domestic energy control methodology and equipment has not significantly altered for decades. However, computer hardware and software has since then flourished to an unprecedented proportion and has become relatively cheap and versatile; these factors pave the way for the application of computer technology in this area of great potential. The technology allows the implementation of a 'hot water energy management system', which makes a forecast of the hot water demand for the next 24 hours and proceeds to provide this demand in the most efficient manner possible. In the (near) future, the system, known as FEMS for Fluid Energy Management System, is able to take advantage and in fact will promote the use of a retail 'dynamic spot price tariff’. FEMS is a combination of hardware and software developed to replace the existing cylinder thermostat, take care of the necessary data-acquisition and control the cylinder's total energy instead of it's (single point) temperature. This provides, besides heating cost reduction, a greater accuracy, a degree of flexibility, improved feedback, legionella inhibition, and a diagnostic capability. To the domestic consumer the latter three items are of greatest relevance. The crux of the system lies in its predictive ability. Having explored the more conventional alternatives, a suitable solution was found in the utilisation of the Elman recurrent neural networks, which focus on the temporal characteristics of the hot water demand time series and are able to adapt to changing environments, coping with the presence of any non-linearity and noise in the data. Prior to developing FEMS a study was made of the basic fluid behaviour in medium and high pressure domestic hot water cylinders, an area not well-covered to date and of interest to engineers and manufacturers alike. For this step data acquisition equipment and software was purposely created. The control software plus equipment were combined into a fully automated test system with minimal operator input, allowing a large amount of data to be gathered over a period measured in months. A similar system was subsequently used to collect actual hot water demand data from a residential family, and in fact forms the basis for FEMS. Finally an enhanced version of FEMS is discussed and it is shown how the system is able to output multiple prediction and utilise varying tariff rates

WOFEX 2021 : 19th annual workshop, Ostrava, 1th September 2021 : proceedings of papers

The workshop WOFEX 2021 (PhD workshop of Faculty of Electrical Engineer-ing and Computer Science) was held on September 1st September 2021 at the VSB – Technical University of Ostrava. The workshop offers an opportunity for students to meet and share their research experiences, to discover commonalities in research and studentship, and to foster a collaborative environment for joint problem solving. PhD students are encouraged to attend in order to ensure a broad, unconfined discussion. In that view, this workshop is intended for students and researchers of this faculty offering opportunities to meet new colleagues.Ostrav

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen