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Speaker adaptation is important to build robust automatic speech recognition (ASR) systems. In this work, we investigate various methods for speaker adaptive training (SAT) based on feature-space approaches for a conformer-based acoustic model (AM) on the Switchboard 300h dataset. We propose a method, called Weighted-Simple-Add, which adds weighted speaker information vectors to the input of the multi-head self-attention module of the conformer AM. Using this method for SAT, we achieve 3.5% and 4.5% relative improvement in terms of WER on the CallHome part of Hub5'00 and Hub5'01 respectively. Moreover, we build on top of our previous work where we proposed a novel and competitive training recipe for a conformer-based hybrid AM. We extend and improve this recipe where we achieve 11% relative improvement in terms of word-error-rate (WER) on Switchboard 300h Hub5'00 dataset. We also make this recipe efficient by reducing the total number of parameters by 34% relative.

New Jersey, NJ -- (SBWIRE) -- 06/24/2022 -- Latest survey on Artificial Intelligence in Accounting Market is conducted to provide hidden gems performance analysis of Artificial Intelligence in Accounting to better demonstrate competitive environment . The study is a mix of quantitative market stats and qualitative analytical information to uncover market size revenue breakdown by key business segments and end use applications. The report bridges the historical data from 2016 to 2021 and forecasted till 2028*, the outbreak of latest scenario in Artificial Intelligence in Accounting market have made companies uncertain about their future outlook as the disturbance in value chain have made serious economic slump. Some are the key & emerging players that are part of coverage and profiled in the study are Microsoft (US), AWS (US), Xero (New Zealand), Intuit (US), Sage (England), OSP (US), UiPath (US), Kore.ai (US), AppZen (US), YayPay (US), IBM (US), Google (US), EY (UK), Deloitte (US), PwC (UK), KPMG (Netherlands), SMACC (Germany), OneUp (US), Vic.ai (US), Hyper Anna (Australia), Botkeeper (US) & MindBridge Analytics (Canada). If you are part of the Artificial Intelligence in Accounting industry or intend to be, then study would provide you comprehensive outlook.

Zscaler, Inc. (NASDAQ: ZS), the chief in cloud safety, at present introduced newly superior AI/ML improvements powered by the most important safety cloud on the earth for unparalleled consumer safety and digital expertise monitoring. The brand new capabilities additional improve Zscaler's Zero Belief Trade safety platform to allow organizations to implement a Safety Service Edge (SSE) that protects towards essentially the most superior cyberattacks, whereas delivering an distinctive digital expertise to customers, and simplifying adoption of a zero belief structure. Organizations are dealing with a 314 % enhance in cyberattacks on encrypted web site visitors and an 80 % enhance in ransomware with almost a 120 % enhance in double extortion assaults. Phishing can be on the rise with industries like monetary companies, authorities and retail seeing annual will increase in assaults of over 100 % in 2021. To fight advancing threats, organizations have to adapt their defenses to real-time modifications in threat.

The New South Wales government has named the 11 individuals who will form the NSW Artificial Intelligence Advisory Committee and play a role in how AI is used in the state. He will be joined by Microsoft Australia national technology officer Lee Hickin; Services Australia chief data officer Maria Milosavljevic; Australian Human Rights Commission human rights commissioner Edward Santow; Women in Data Science Network Sydney ambassador and School of Illinois data and AI research fellow Theresa Anderson; University of Technology Sydney data science executive director Fang Chen; Innovations Accelerated chief legal and data ethics officer Aurelie Jacquet; Australian Computer Society AI and ethics technical committee chair Peter Leonard; Gradient Institute co-founder William (Bill) Simpson Young; Quantium Health and Government CEO Neil Soderlund; and Public Purpose principal Martin Stewart-Weeks. Minister for Customer Service Victor Dominello said the committee would advise the state government on the use of AI for decision-making and service delivery, and what ethical AI policies should look like. "AI is becoming more prevalent in our day-to-day life and the NSW Government is determined to lead the way in its use and to drive improvements wherever possible, while ensuring it's done in an ethical way." Establishing the committee is part of the state government's AI strategy in which it has pledged that transparency will be the focus and vowed to make the state the digital capital of the southern hemisphere in the next three years.

To solve the conundrum of how to get timely medical care to people with memory loss or other impaired cognitive functioning, a new study suggests that artificial intelligence may be as accurate as clinicians in taking the first step: diagnosis. Findings from the study, which was conducted by researchers at Boston University School of Medicine, were published online Monday in the journal Nature Communications. "We're trying to leverage AI to create frameworks to mimic neurology experts," for dementia diagnosis, Vijaya B. Kolachalama, the study's principal investigator and assistant professor of medicine and computer science at Boston University, told UPI. He said his lab aims to use computer models to assist clinical practice. Kolachalama stressed that the aim of his team's work is to help reduce the workload of the busy neurology practice, not replace the expert clinician.

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B, not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate. Recommender systems are a key technology in online platforms for personalizing the selection of items that are shown to a user. Examples include recommending which products to buy, which videos to watch or which songs to play. Recommendations are typically user-dependent and often context-dependent. A key operation of recommender systems is to retrieve a ranked list of the best items for a user in a particular context.

Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph completion (KGC). However, existing KGC methods suffer from two main issues, 1) the false negative issue, i.e., the sampled negative training instances may include potential true facts; and 2) the data sparsity issue, i.e., true facts account for only a tiny part of all possible facts. To this end, we propose positive-unlabeled learning with adversarial data augmentation (PUDA) for KGC. In particular, PUDA tailors positive-unlabeled risk estimator for the KGC task to deal with the false negative issue. Furthermore, to address the data sparsity issue, PUDA achieves a data augmentation strategy by unifying adversarial training and positive-unlabeled learning under the positive-unlabeled minimax game. Extensive experimental results on real-world benchmark datasets demonstrate the effectiveness and compatibility of our proposed method.

Optimal transport (OT) is known to be sensitive against outliers because of its marginal constraints. Outlier robust OT variants have been proposed based on the definition that outliers are samples which are expensive to move. In this paper, we show that this definition is restricted by considering the case where outliers are closer to the target measure than clean samples. We show that outlier robust OT fully transports these outliers leading to poor performances in practice. To tackle these outliers, we propose to detect them by relying on a classifier trained with adversarial training to classify source and target samples. A sample is then considered as an outlier if the prediction from the classifier is different from its assigned label. To decrease the influence of these outliers in the transport problem, we propose to either remove them from the problem or to increase the cost of moving them by using the classifier prediction. We show that we successfully detect these outliers and that they do not influence the transport problem on several experiments such as gradient flows, generative models and label propagation.

We consider the problem of reaching a propositional goal condition in fully-observable nondeterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A sound and complete FOND+ planner is implemented by reducing FOND+ planning to answer set programs, and its performance is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools. Two other FOND+ planners are introduced as well which are more scalable but are not complete.