Abstract The Interspeech 2015 Zero Resource Speech Challenge aims at discovering
subword and word units from raw speech. The challenge provides the first unified and open …

Spoken term detection (STD) provides an efficient means for content based indexing of
speech. However, achieving high detection performance, faster speed, detecting ot-of …

Automatic detection of phoneme or word-like units is one of the core objectives in zero-
resource speech processing. Recent attempts employ self-supervised training methods …

Recent progress in self-supervised or unsupervised machine learning has opened the
possibility of building a full speech processing system from raw audio without using any …

Spoken content retrieval refers to directly indexing and retrieving spoken content based on
the audio rather than text descriptions. This potentially eliminates the requirement of …

Typically, unsupervised segmentation of speech into the phone-and wordlike units are
treated as separate tasks and are often done via different methods which do not fully …

We adopt a Dirichlet process Gaussian mixture model (DPGMM) for unsupervised acoustic
modeling and represent speech frames with Gaussian posteriorgrams. The model performs …

Reading comprehension by machine has been widely studied, but machine comprehension
of spoken content is still a less investigated problem. In this paper, we release Open-Domain …

This paper presents a study of spectral clustering-based approaches to acoustic segment
modeling (ASM). ASM aims at finding the underlying phoneme-like speech units and …

For many languages in the world, not enough (annotated) speech data is available to train
an ASR system. We here propose a new three-step method to build an ASR system for such …