Evaluation of Crowdsourced User Input Data for Spoken Dialog Systems

Using the Internet for the collection of data is quite common these days. This process is called crowdsourcing and enables the collection of large amounts of data at reasonable costs. While being an inexpensive method, this data typically
is of lower quality. Filtering data sets is therefore required. The occurring errors can be classified into different groups. There are technical issues and human errors. For speech recording, technical issues could be a noisy background. Human errors arise when the task is misunderstood. We employ several techniques for recognizing errors and eliminating faulty data sets in user input data for a Spoken Dialog System (SDS). Furthermore, we compare three different kinds of questionnaires (QNRs) for a given set of seven tasks. We analyze the characteristics of the resulting data sets and give a recommendation which type of QNR might be the most suitable one for a given purpose.