Machine learning is one of the most important and successful techniques in contemporary computer science. It involves the statistical inference of models (such as classifiers) from data. It is often conceived in a very impersonal way, with algorithms working autonomously on passively collected data. However, this viewpoint hides considerable human work of tuning the algorithms, gathering the data, and even deciding what should be modeled in the first place. Examining machine learning from a human-centered perspective includes explicitly recognising this human work, as well as reframing machine learning workflows based on situated human working practices, and exploring the coadaptation of humans and systems. A human-centered understanding of machine learning in human context can lead not only to more usable machine learning tools, but to new ways of framing learning computationally. This workshop will bring together researchers to discuss these issues and suggest future research questions aimed at creating a human-centered approach to machine learning.

The problem of pitch tracking has been extensively studied in the speech research community. The goal of this paper is to investigate how these techniques should be adapted to singing voice analysis, and to provide a comparative evaluation of the most representative state-of-the-art approaches. This study is carried out on a large database of annotated singing sounds with aligned EGG recordings, comprising a variety of singer categories and singing exercises. The algorithmic performance is assessed according to the ability to detect voicing boundaries and to accurately estimate pitch contour. First, we evaluate the usefulness of adapting existing methods to singing voice analysis. Then we compare the accuracy of several pitchextraction algorithms, depending on singer category and laryngeal mechanism. Finally, we analyze their robustness to reverberation.

In this paper, we present a modified version of HTS, called performative HTS or pHTS. The objective of pHTS is to enhance the control ability and reactivity of HTS. pHTS reduces the phonetic context used for training the models and generates the speech parameters within a 2-label window. Speech waveforms are generated on-the-fly and the models can be reactively modified, impacting the synthesized speech with a delay of only one phoneme. It is shown that HTS and pHTS have comparable output quality. We use this new system to achieve reactive model interpolation and conduct a new test where articulation degree is modified within the sentence.

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