Document Level Semantic Context for Retrieving OOV Proper Names

International audienceRecognition of Proper Names (PNs) in speech is important for content based indexing and browsing of audio-video data.However, many PNs are Out-Of-Vocabulary (OOV) words nfor LVCSR systems used in these applications due to the diachronicnature of data. By exploiting semantic context of the audio, relevant OOV PNs can be retrieved and then the target PNs can be recovered. To retrieve OOV PNs, we propose to represent their context with document level semantic vectors; and show that this approach is able to handle less frequent OOV PNs in the training data. We study different representations, including Random Projections, LSA, LDA, Skip-gram, CBOW and GloVe. A further evaluation of recovery of target OOV PNs using a phonetic search shows that document level semantic context is reliable for recovery of OOV PNs

Improved Neural Bag-of-Words Model to Retrieve Out-of-Vocabulary Words in Speech Recognition

International audienceMany Proper Names (PNs) are Out-Of-Vocabulary (OOV) words for speech recognition systems used to process di-achronic audio data. To enable recovery of the PNs missed by the system, relevant OOV PNs can be retrieved by exploiting the semantic context of the spoken content. In this paper, we explore the Neural Bag-of-Words (NBOW) model, proposed previously for text classification, to retrieve relevant OOV PNs. We propose a Neural Bag-of-Weighted-Words (NBOW2) model in which the input embedding layer is augmented with a context anchor layer. This layer learns to assign importance to input words and has the ability to capture (task specific) keywords in a NBOW model. With experiments on French broadcast news videos we show that the NBOW and NBOW2 models outper-form earlier methods based on raw embeddings from LDA and Skip-gram. Combining NBOW with NBOW2 gives faster convergence during training

AI-assisted patent prior art searching - feasibility study

This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy

AI-assisted patent prior art searching - feasibility study

This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy