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A summary of the 2012 JHU CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.5 page(s
Learning An Invariant Speech Representation
Recognition of speech, and in particular the ability to generalize and learn
from small sets of labelled examples like humans do, depends on an appropriate
representation of the acoustic input. We formulate the problem of finding
robust speech features for supervised learning with small sample complexity as
a problem of learning representations of the signal that are maximally
invariant to intraclass transformations and deformations. We propose an
extension of a theory for unsupervised learning of invariant visual
representations to the auditory domain and empirically evaluate its validity
for voiced speech sound classification. Our version of the theory requires the
memory-based, unsupervised storage of acoustic templates -- such as specific
phones or words -- together with all the transformations of each that normally
occur. A quasi-invariant representation for a speech segment can be obtained by
projecting it to each template orbit, i.e., the set of transformed signals, and
computing the associated one-dimensional empirical probability distributions.
The computations can be performed by modules of filtering and pooling, and
extended to hierarchical architectures. In this paper, we apply a single-layer,
multicomponent representation for phonemes and demonstrate improved accuracy
and decreased sample complexity for vowel classification compared to standard
spectral, cepstral and perceptual features.Comment: CBMM Memo No. 022, 5 pages, 2 figure
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