5 accepted papers
K-12 school classrooms have proven to be a challenging environment for Automatic Speech Recognition (ASR) systems, both due to background noise and conversation, and differences in linguistic and acoustic properties from adult speech, on which the majority of ASR systems are trained and evaluated. W…
We propose a new method for speaker diarization that can handle overlapping speech with 2+ people. Our method is based on compositional embeddings [1]: Like standard speaker embedding methods such as x-vector [2], compositional embedding models contain a function f that separates speech from differe…
With the goal of giving teachers automated feedback about their classrooms, we investigate how to train automatic speech detectors of key phrases such as good job, thank you, please, and you’re welcome. This kind of language conveys support and respect from teacher to student and is one of the behav…
The accuracy of speaker verification and diarization models depends on the quality of the speaker embeddings used to separate audio samples from different speakers. With the goal of training better embedding models, we devise an automatic pipeline for large-scale collection of speech samples from un…
Automatic machine learning-based detectors of various psychological and social phenomena (e.g., emotion, stress, engagement) have great potential to advance basic science. However, when a detector d is trained to approximate an existing measurement tool (e.g., a questionnaire, observation protocol),…