The benefits of contextual information for speech recognition systems

This paper demonstrates the significance of using contextual information in machine learning and speech recognition. While the benefits of contextual information in human communication are widely known, their significance is rarely explored or discussed with a view to their potential for improving speech recognition accuracy. The presented research primarily focuses on an undertaken empirical study that looks at how context affects human communication and understanding. During the study, comparisons between human communication with and without context, have shown overall recognition improvements of over 30% when contextual information is provided. The study has also investigated the importance of the former/middle/latter part of a word towards recognition. These results show that the first two-thirds of a spoken word are key for humans to correctly infer a word. The conclusions from the performed study are then drawn upon to identify useful types of context that can help a machine’s understanding, and how such contextual information can be gathered in speech recognition and machine learning systems. This paper shows that context is not only highly important for human communication, but can easily provide a wealth of information to enhance computational systems

Requirement engineering for context aware applications: a proceeding for context elements identification and representation

A few years ago, it seemed inconceivable to think about cars able to detect open doors automatically, with a device for speech recognition; besides, it was almost unbelievable to imagine houses that close their windows in case of rain, or heating systems that turn themselves on at a specific time, reaching certain temperatures; among other characteristics. However, nowadays it is almost natural to have these benefits at our disposal; even it is possible to abstract oneself about the hardware used for their implementation. This fact is due to the technical advance, as well as to the raise of a new paradigm: Context Aware Programming In other words, the development of applications aimed to react automatically towards environment changes. This type of application requires a representation scheme over the contextual information used. This paper defines some guidelines connected to Requirement Engineering for these systems to operate. First, a context taxonomy is conceptualized, used as a guide for eliciting processes; then, definitions for “element”, “context attribute”, and “representation scheme” are presented. Finally, a procedure for eliciting and specifying context is proposed.VI Workshop Ingeniería de Software (WIS)Red de Universidades con Carreras en Informática (RedUNCI

Contextual Language Model Adaptation for Conversational Agents

Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents. These ASR systems should provide a high accuracy under a variety of speaking styles, domains, vocabulary and argots. In this paper, we present a DNN-based method to adapt the LM to each user-agent interaction based on generalized contextual information, by predicting an optimal, context-dependent set of LM interpolation weights. We show that this framework for contextual adaptation provides accuracy improvements under different possible mixture LM partitions that are relevant for both (1) Goal-oriented conversational agents where it's natural to partition the data by the requested application and for (2) Non-goal oriented conversational agents where the data can be partitioned using topic labels that come from predictions of a topic classifier. We obtain a relative WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass decoding framework, over an unadapted model. We also show up to a 15% relative improvement in recognizing named entities which is of significant value for conversational ASR systems.Comment: Interspeech 2018 (accepted

Multimodal and ubiquitous computing systems: supporting independent-living older users

We document the rationale and design of a multimodal interface to a pervasive/ubiquitous computing system that supports independent living by older people in their own homes. The Millennium Home system involves fitting a resident’s home with sensors – these sensors can be used to trigger sequences of interaction with the resident to warn them about dangerous events, or to check if they need external help. We draw lessons from the design process and conclude the paper with implications for the design of multimodal interfaces to ubiquitous systems developed for the elderly and in healthcare, as well as for more general ubiquitous computing applications

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed