Emotion recognition is important at the workplace because it impacts a multitude of outcomes, such as performance, engagement and well-being. Emotion recognition from audio is an attractive option due to its non-obtrusive nature and availability of microphones in devices at the workplace. We describe building a classifier that analyzes the para-linguistic features of audio streams to classify them into positive, neutral and negative affect. Since speech at the workplace is different from acted speech, and because it is important that the training data be situated in the right context, we designed and executed an emotion induction procedure to generate a corpus of non-acted speech data of 33 speakers. The corpus was used to train a set of classification models and a comparative analysis of these models was used to choose the feature parameters. Bootstrap aggregation (bagging) was then used on the best combination of algorithm (Random Forest) and features (60 millisecond window size). The resulting classification accuracy of 73% is on par with, or exceeds, accuracies reported in the current literature for non-acted speech for a speaker-dependent test set. For reference, we also report the speaker-dependent recognition accuracy (95%) of the same classifier trained and tested on acted speech for three emotions in the Emo-DB database.

Designers of IVR systems often shy away from using speech prompts; preferring, where they can, to use keypad input. Part of the reason is that speech processing is expensive and often error prone. This work attempts to address this problem by offering guidelines for prompt design based on field experiments. It is shown, specifically, that accuracy can be influenced by prompt examples, depending on the nature of the information requested.

Poor academic performance in K-12 is often a precursor to unsatisfactory educational outcomes such as dropout, which are associated with significant personal and social costs. Hence, it is important to be able to predict students at risk of poor performance, so that the right personalized intervention plans can be initiated. In this paper, we report on a large-scale study to identify students at risk of not meeting acceptable levels of performance in one state-level and one national standardized assessment in Grade 8 of a major US school district. An important highlight of our study is its scale - both in terms of the number of students included, the number of years and the number of features, which provide a very solid grounding to the research. We report on our experience with handling the scale and complexity of data, and on the relative performance of various machine learning techniques we used for building predictive models. Our results demonstrate that it is possible to predict students at-risk of poor assessment performance with a high degree of accuracy, and to do so well in advance. These insights can be used to pro-actively initiate personalized intervention programs and improve the chances of student success.

Designing interactive voice systems that have optimum cognitive load on callers has been an active research topic for quite some time. There have been many studies comparing the user preferences on navigation trees with higher depths over higher breadths. In this paper, we consider the navigation of structured data containing various types of attributes using phone-based interactions. This problem is particularly relevant to emerging economies in which innovative voice-based applications are being built to address semi-literate population. We address the problem of identifying the right sequence of facets to be presented to the user for phone-based navigation of the data in two stages. Firstly, we perform extensive user studies in the target population to understand the relation between the nature of facets (attributes) of the data and the cognitive load. Secondly, we propose an algorithm to design optimum navigation trees based on the inferences made in the first phase. We compare the proposed algorithm with the traditional facet generation algorithms with respect to various factors and discuss the optimality of the proposed algorithm.

We present observations from various user studies that have helped to guide the design and deployment of a voice-based employment platform. Specifically, we present findings as a function of demographics, considering employers and candidates across two regions of Karnataka. Our results are of use to those building interfaces to address rural unemployment in particular, and those looking to understand the employment landscape of rural India in general. Our main contributions are motivating the need for such a platform in this context, and offering insight into the engineering of job matches.