We propose improved Deep Neural Network (DNN) training loss functions for more accurate single keyword spotting on resource-constrained embedded devices. The loss function modifications consist of a combination of multi-task training and weighted cross entropy. In the multi-task architecture, the keyword DNN acoustic model is trained with two tasks in parallel the main task of predicting the ke...

Support Vector machines (SVM) is a new and very promising classification technique developed from the theory of Structural Risk Minimisation [1]. In this paper, we propose an alternative out-of-vocabulary word detection method relying on confidence measures and support vector machines. Confidence measures are computed from phone level information provided by a Hidden Markov Model (HMM) based sp...

In this paper we investigate a time delay neural network (TDNN) for a keyword spotting task that requires low CPU, memory and latency. The TDNN is trained with transfer learning and multi-task learning. Temporal subsampling enabled by the time delay architecture reduces computational complexity. We propose to apply singular value decomposition (SVD) to further reduce TDNN complexity. This allow...

We propose a novel architecture for keyword spotting which is composed of a Dynamic Bayesian Network (DBN) and a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net. The DBN uses a hidden garbage variable as well as the concept of switching parents to discriminate between keywords and arbitrary speech. Contextual information is incorporated by a BLSTM network, providing a discrete...

Keyword spotting (KWS) is a crucial function enabling the interaction with many ubiquitous smart devices in our surroundings, either activating them through wake-word or directly as human-computer interface. For applications, KWS entry point for interactions device and, thus, an always-on workload. Many are mobile and their battery lifetime heavily impacted by continuously running services. sim...

Recent advances in on-line data capturing technologies and its widespread deployment in devices like PDAs and notebook PCs is creating large amounts of handwritten data that need to be archived and retrieved efficiently. Word-spotting, which is based on a direct comparison of a handwritten keyword to words in the document, is commonly used for indexing and retrieval. We propose a string matchin...

• We adapt a traditional non-learnable GED algorithm to the novel paradigm of geometric deep learning. Triplet network for learning graph distances by means neural networks. Learning distance in domain without an embedding stage. Graph-based keyword spotting application with state-of-the-art performance. The emergence as framework deal graph-based representations has faded away approaches favor...

This paper describes a method for script independent word spotting in multilingual handwritten and machine printed documents. The system accepts a query in the form of text from the user and returns a ranked list of word images from document image corpus based on similarity with the query word. The system is divided into two main components. The first component known as Indexer, performs indexi...