Publications by Maria Wolters

Background: Altered physical activity is an important feature of depression. It is manifested in psychomotor retardation, agitation and withdrawal from engagement in normal activities. Modern devices for activity monitoring (actigraphs) make it possible to monitor physical activity unobtrusively but the validity of actigraphy as an indicator of mood state is uncertain. We carried out a systematic review of digital actigraphy in patients with depression to investigate the associations between measured physical activity and depression. Methods: Systematic review and meta-analysis. Studies were identified from Medline, EMBASE and Psycinfo databases and included if they were either case control or longitudinal studies of actigraphy in adults aged between 18 and 65 diagnosed with a depressive disorder. Outcomes were daytime and night-time activity and actigraphic measures of sleep. Results: We identified 19 eligible papers from 16 studies (412 patients). Case control studies showed less daytime activity in patients with depression (standardised mean difference −0.76, 95 intervals −1.05 to −0.47). Longitudinal studies showed moderate increase in daytime activity (0.53, 0.20 to 0.87) and a reduction in night-time activity (−0.36, −0.65 to −0.06) over the course of treatment. Limitations: All study participants were unblinded. Only seven papers included patients treated in the community. Conclusions: Actigraphy is a potentially valuable source of additional information about patients with depression. However, there are no clear guidelines for use of actigraphy in studies of patients with depression. Further studies should investigate patients treated in the community. Additional work to develop algorithms for differentiating behaviour patterns is also needed.

Keywords: irrelevant speech effect, reminders, spearcon, speech, working memory

When screening participants for speech perception experiments, formal audiometric screens are often not an option, especially when studies are conducted over the Internet. We investigated whether a brief standardized self-report questionnaire, the screening version of the Hearing Handicap Inventory for Adults (HHIA-S), could be used to approximate the results of audiometric screening. Our results suggest that while the HHIA-S is useful, it needs to be used with extremely strict cut-off values that could exclude around 25% of people with no hearing impairment who are interested in participating. Well constructed, standardized single questions might be a more feasible alternative, in particular for web experiments.

A common task for spoken dialog systems (SDS) is to help users select a suitable option (e.g., flight, hotel, and restaurant) from the set of options available. As the number of options increases, the system must have strategies for generating summaries that enable the user to browse the option space efficiently and successfully. In the user-model based summarize and refine approach (UMSR, Demberg and Moore, 2006), options are clustered to maximize utility with respect to a user model, and linguistic devices such as discourse cues and adverbials are used to highlight the trade-offs among the presented items. In a Wizard-of-Oz experiment, we show that the UMSR approach leads to improvements in task success, efficiency, and user satisfaction compared to an approach that clusters the available options to maximize coverage of the domain (Polifroni et al., 2003). In both a laboratory experiment and a web-based experimental paradigm employing the Amazon Mechanical Turk platform, we show that the discourse cues in UMSR summaries help users compare different options and choose between options, even though they do not improve verbatim recall. This effect was observed for both written and spoken stimuli.

We present the MATCH corpus, a unique data set of 447 dialogues in which 26 older and 24 younger adults interact with nine different spoken dialogue systems. The systems varied in the number of options presented and the confirmation strategy used. The corpus also contains information about the users' cognitive abilities and detailed usability assessments of each dialogue system. The corpus, which was collected using a Wizard-of-Oz methodology, has been fully transcribed and annotated with dialogue acts and “Information State Update” (ISU) representations of dialogue context. Dialogue act and ISU annotations were performed semi-automatically. In addition to describing the corpus collection and annotation, we present a quantitative analysis of the interaction behaviour of older and younger users and discuss further applications of the corpus. We expect that the corpus will provide a key resource for modelling older people's interaction with spoken dialogue systems.

Keywords: Spoken dialogue corpora, Spoken dialogue systems, Cognitive ageing, Annotation, Information states, Speech acts, User simulations, Speech recognition

Electronic reminders can play a key role in enabling people to manage their care and remain independent in their own homes for longer. The MultiMemoHome project aims to develop reminder designs that are accessible and usable for users with a range of abilities and preferences. In an initial exploration of key design parameters, we surveyed 378 adults from all age groups online (N=206) and by post (N= 172). The wide spread of preferences that we found illustrates the importance of adapting reminder solutions to individuals. We present two reusable personas that emerged from the research and discuss how questionnaires can be used for technology transfer.

It is well known that help prompts shape how users talk to spoken dialogue systems. This study investigated the effect of help prompt placement on older users' interaction with a smart home interface. In the dynamic help condition, help was only given in response to system errors; in the inherent help condition, it was also given at the start of each task. Fifteen older and sixteen younger users interacted with a smart home system using two different scenarios. Each scenario consisted of several tasks. The linguistic style users employed to communicate with the system (interaction style) was measured using the ratio of commands to the overall utterance length (keyword ratio) and the percentage of content words in the user's utterance that could be understood by the system (shared vocabulary). While the timing of help prompts did not affect the interaction style of younger users, it was early task-specific help supported older users in adapting their interaction style to the system's capabilities. Well-placed help prompts can significantly increase the usability of spoken dialogue systems for older people.

Microtask platforms such as Amazon Mechanical Turk (AMT) are increasingly used to create speech and language resources. AMT in particular allows researchers to quickly recruit a large number of fairly demographically diverse participants. In this study, we investigated whether AMT can be used for comparing the intelligibility of speech synthesis systems. We conducted two experiments in the lab and via AMT, one comparing US English diphone to US English speaker-adaptive HTS synthesis and one comparing UK English unit selection to UK English speaker-dependent HTS synthesis. While AMT word error rates were worse than lab error rates, AMT results were more sensitive to relative differences between systems. This is mainly due to the larger number of listeners. Boxplots and multilevel modelling allowed us to identify listeners who performed particularly badly, while thresholding was sufficient to eliminate rogue workers. We conclude that AMT is a viable platform for synthetic speech intelligibility comparisons.

Older adults are a challenging user group because their behaviour can be highly variable. To the best of our knowledge, this is the first study where dialogue strategies are learned and evaluated with both simulated younger users and simulated older users. The simulated users were derived from a corpus of interactions with a strict system-initiative spoken dialogue system (SDS). Learning from simulated younger users leads to a policy which is close to one of the dialogue strategies of the underlying SDS, while the simulated older users allow us to learn more flexible dialogue strategies that accommodate mixed initiative. We conclude that simulated users are a useful technique for modelling the behaviour of new user groups.

User quality judgements can show a bewildering amount of variation that is diffcult to capture using traditional quality prediction approaches. Using clustering, an ex- ploratory statistical analysis technique, we reanalysed the data set of a Wizard-of-Oz experiment where 25 users were asked to rate the dialogue after each turn. The sparse data problem was addressed by careful a priori parameter choices and comparison of the results of different cluster algorithms. We found two distinct classes of users, positive and critical. Positive users were generally happy with the dialogue system, and did not mind errors. Critical users downgraded their opinion of the system after errors, used a wider range of ratings, and were less likely to rate the system positively overall. These user groups could not be predicted by experience with spoken dialogue systems, attitude to spoken dialogue systems, anity with technology, demographics, or short-term memory capacity. We suggest that evaluation research should focus on critical users and discuss how these might be identified.

When deciding whether to adapt relevant aspects of the system to the particular needs of older users, spoken dialogue systems often rely on automatic detection of chronological age. In this paper, we show that vocal ageing as measured by acoustic features is an unreliable indicator of the need for adaptation. Simple lexical features greatly improve the prediction of both relevant aspects of cognition and interactions style. Lexical features also boost age group prediction. We suggest that adaptation should be based on observed behaviour, not on chronological age, unless it is not feasible to build classifiers for relevant adaptation decisions.

Although older people are an important user group for smart environments, there has been relatively little work on adapting natural language interfaces to their requirements. In this paper, we focus on a particularly thorny problem: processing speech input from older users. Our experiments on the MATCH corpus show clearly that we need age-specific adaptation in order to recognize older users' speech reliably. Language models need to cover typical interaction patterns of older people, and acoustic models need to accommodate older voices. Further research is needed into intelligent adaptation techniques that will allow existing large, robust systems to be adapted with relatively small amounts of in-domain, age appropriate data. In addition, older users need to be supported with adequate strategies for handling speech recognition errors.

Most studies on adapting voice interfaces to older users work top-down by comparing the interaction behavior of older and younger users. In contrast, we present a bottom-up approach. A statistical cluster analysis of 447 appointment scheduling dialogs between 50 older and younger users and 9 simulated spoken dialog systems revealed two main user groups, a “social” group and a “factual” group. “Factual” users adapted quickly to the systems and interacted efficiently with them. “Social” users, on the other hand, were more likely to treat the system like a human, and did not adapt their interaction style. While almost all “social” users were older, over a third of all older users belonged in the “factual” group. Cognitive abilities and gender did not predict group membership. We conclude that spoken dialog systems should adapt to users based on observed behavior, not on age.

We evaluated two strategies for alleviating working memory load for users of voice interfaces: presenting fewer options per turn and providing confirmations. Forty-eight users booked appointments using nine different dialogue systems, which varied in the number of options presented and the confirmation strategy used. Participants also performed four cognitive tests and rated the usability of each dialogue system on a standardised questionnaire. When systems presented more options per turn and avoided explicit confirmation subdialogues, both older and younger users booked appointments more quickly without compromising task success. Users with lower information processing speed were less likely to remember all relevant aspects of the appointment. Working memory span did not affect appointment recall. Older users were slightly less satisfied with the dialogue systems than younger users. We conclude that the number of options is less important than an accurate assessment of the actual cognitive demands of the task at hand.

This paper summarises the main results of a pilot study into the effect of auditory ageing on the intelligibility of synthetic speech. 32 older and 12 younger users had to answer simple questions about a series of meeting reminders and medication reminders. They also underwent an extensive battery of audiological and cognitive assessments. Older users only had more difficulty understanding the synthetic voice than younger people if they had elevated pure-tone thresholds and if they were asked to unfamiliar medication names. We suggest that these problems can be remedied by better prompt design. User interviews show that the synthetic voice used was quite natural. Problems mentioned by users fit the results of a previous error analysis.

Many factors affect the intelligibility of synthetic speech. One aspect that has been severely neglected in past work is hearing loss. In this study, we investigate whether pure-tone audiometry thresholds across a wide range of frequencies (0.25-20kHz) are correlated with participants� performance on a simple task that involves accurately recalling and processing reminders. Participants� scores correlate not only with thresholds in the frequency ranges commonly associated with speech, but also with extended high-frequency thresholds.

In this paper, we report on an experiment that tested users� ability to understand the content of spoken auditory reminders. Users heard meeting reminders and medication reminders spoken in both a natural and a synthetic voice. Our results show that older users can understand synthetic speech as well as younger users provided that the prompt texts are well-designed, using familiar words and contextual cues. As soon as unfamiliar and complex words are introduced, users� hearing affects how well they can understand the synthetic voice, even if their hearing would pass common screening tests for speech synthesis experiments. Although hearing thresholds correlate best with users� performance, central auditory processing may also influence performance, especially when complex errors are made.

Hearing loss as assessed by pure-tone audiometry (PTA) is significantly correlated with the intelligibility of synthetic speech. However, PTA is a subjective audiological measure that assesses the entire auditory pathway and does not discriminate between the different afferent and efferent contributions. In this paper, we focus on one particular aspect of hearing that has been shown to correlate with hearing loss: outer hair cell (OHC) function. One role of OHCs is to increase sensitivity and frequency selectivity. This function of OHCs can be assessed quickly and objectively through otoacoustic emissions (OAE) testing, which is little known outside the field of audiology. We find that OHC function affects the perception of human speech, but not that of synthetic speech. This has important implications not just for audiological and electrophysiological research, but also for adapting speech synthesis to ageing ears.