Despite significant improvements in automatic speech recognition and spoken language understanding - human interaction with Virtual Personal Assistants (VPAs) through speech remains irregular and sporadic. According to recent studies, currently the usage of VPAs is constrained to basic tasks such as checking facts, playing music, and obtaining weather updates.In this paper, we present results of a survey (N = 118) that analyses usage of VPAs by frequent and infrequent users. We investigate how usage experience, performance expectations, and privacy concerns differ between these two groups. The results indicate that, compared with infrequent users, frequent users of VPAs are more satisfied with their assistants, more eager to use them in a variety of settings, yet equally concerned about their privacy.
Intelligent Personal Assistants were originally developed to make the interaction with computer systems more human-like, enabling people to use natural language to manage their schedules, and access a variety of tasks and services @cite_7 . The present generation of such assistants (e.g. Cortana, Siri, etc.), often referred to as Voice Powered Assistants (VPAs) is the focus of our study. The growing popularity of VPAs and their improved accessibility can be attributed to the latest advances in speech technology. In recent years, the introduction of deep neural networks (DNN) for acoustic and language modelling has made ‘automatic speech recognition’ (ASR) systems more robust @cite_5 @cite_10 , while the implementation of the knowledge graph enhanced the ‘spoken language understanding’ (SLU) capabilities of question answering systems @cite_8 . Recent studies in the area addressed usage patterns of virtual assistants and problems with their adoption. The evaluation of VPAs focused on different aspects that affect their continued use, such as usage experience @cite_0 @cite_1 @cite_20 , performance expectations @cite_11 , privacy concerns and social acceptability @cite_19 @cite_9 . We discuss these studies and their findings below.
@cite_7 @cite_8 @cite_9 @cite_1 @cite_0 @cite_19 @cite_5 @cite_10 @cite_20 @cite_11
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Abstract Speech interfaces to conversational systems have been a focus in academia and industry for over a decade due to its applicability as a natural interface. Speech recognition and speech synthesis constitute the important input and output modules respectively for such spoken interface systems. In this paper, the speech recognition interface for question answering applications is reviewed, and existing limitations are discussed. The existing spoken question answering (QA) systems use an automatic speech recogniser by adapting acoustic and language models for the speech interface and off-the-shelf language processing systems for question interpretation. In the process, the impact of recognition errors and language processing inaccuracies is neglected. It is illustrated in the paper how a semantically rich knowledge graph can be used to solve automatic speech recognition and language processing specific problems. A simple concatenation of a speech recogniser and a natural language processing system is a shallow method for a speech interface. An effort beyond merely concatenating these two units is required to develop a successful spoken question answering system. It is illustrated in this paper how a knowledge graph based structured data can be used to build a unified system combining speech recognition and language understanding. This facilitates the use of a semantically rich data model for speech interface. Voice-controlled intelligent personal assistants, such as Cortana, Google Now, Siri and Alexa, are increasingly becoming a part of users' daily lives, especially on mobile devices. They introduce a significant change in information access, not only by introducing voice control and touch gestures but also by enabling dialogues where the context is preserved. This raises the need for evaluation of their effectiveness in assisting users with their tasks. However, in order to understand which type of user interactions reflect different degrees of user satisfaction we need explicit judgements. In this paper, we describe a user study that was designed to measure user satisfaction over a range of typical scenarios of use: controlling a device, web search, and structured search dialogue. Using this data, we study how user satisfaction varied with different usage scenarios and what signals can be used for modeling satisfaction in the different scenarios. We find that the notion of satisfaction varies across different scenarios, and show that, in some scenarios (e.g. making a phone call), task completion is very important while for others (e.g. planning a night out), the amount of effort spent is key. We also study how the nature and complexity of the task at hand affects user satisfaction, and find that preserving the conversation context is essential and that overall task-level satisfaction cannot be reduced to query-level satisfaction alone. Finally, we shed light on the relative effectiveness and usefulness of voice-controlled intelligent agents, explaining their increasing popularity and uptake relative to the traditional query-response interaction. Intelligent Personal Assistants (IPAs) are widely available on devices such as smartphones. However, most people do not use them regularly. Previous research has studied the experiences of frequent IPA users. Using qualitative methods we explore the experience of infrequent users: people who have tried IPAs, but choose not to use them regularly. Unsurprisingly infrequent users share some of the experiences of frequent users, e.g. frustration at limitations on fully hands-free interaction. Significant points of contrast and previously unidentified concerns also emerge. Cultural norms and social embarrassment take on added significance for infrequent users. Humanness of IPAs sparked comparisons with human assistants, juxtaposing their limitations. Most importantly, significant concerns emerged around privacy, monetization, data permanency and transparency. Drawing on these findings we discuss key challenges, including: designing for interruptability; reconsideration of the human metaphor; issues of trust and data ownership. Addressing these challenges may lead to more widespread IPA use. A review of popular technology adoption models identified several factors that are likely to influence Voice Activated Personal Assistant (VAPA) use in public spaces. To inform design decisions of how to make the private use of the VAPA in public spaces more acceptable from the users’ point of view, an online survey was conducted to investigate the likelihood of usage of the smartphone VAPA such as Apple’s Siri (compared to the usage of smartphone keyboard) as a function of location (private vs. public) and type of information (private vs. nonprivate). Responses from participants showed that users were more cautious of transmitting private than nonprivate information. This effect of type of information was amplified in the social context of public locations and when using conspicuous methods of information input such as the VAPA. Participants also preferred using the VAPA in private locations and showed no preference of location for keyboard entries. Correlations between likelihood of usage of VAPA and th... Convolutional neural networks (CNNs) are a standard component of many current state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, CNNs in LVCSR have not kept pace with recent advances in other domains where deeper neural networks provide superior performance. In this paper we propose a number of architectural advances in CNNs for LVCSR. First, we introduce a very deep convolutional network architecture with up to 14 weight layers. There are multiple convolutional layers before each pooling layer, with small 3x3 kernels, inspired by the VGG Imagenet 2014 architecture. Then, we introduce multilingual CNNs with multiple untied layers. Finally, we introduce multi-scale input features aimed at exploiting more context at negligible computational cost. We evaluate the improvements first on a Babel task for low resource speech recognition, obtaining an absolute 5.77 WER improvement over the baseline PLP DNN by training our CNN on the combined data of six different languages. We then evaluate the very deep CNNs on the Hub5'00 benchmark (using the 262 hours of SWB-1 training data) achieving a word error rate of 11.8 after cross-entropy training, a 1.4 WER improvement (10.6 relative) over the best published CNN result so far. The past four years have seen the rise of conversational agents (CAs) in everyday life. Apple, Microsoft, Amazon, Google and Facebook have all embedded proprietary CAs within their software and, increasingly, conversation is becoming a key mode of human-computer interaction. Whilst we have long been familiar with the notion of computers that speak, the investigative concern within HCI has been upon multimodality rather than dialogue alone, and there is no sense of how such interfaces are used in everyday life. This paper reports the findings of interviews with 14 users of CAs in an effort to understand the current interactional factors affecting everyday use. We find user expectations dramatically out of step with the operation of the systems, particularly in terms of known machine intelligence, system capability and goals. Using Norman's 'gulfs of execution and evaluation' [30] we consider the implications of these findings for the design of future systems.