This chapter provides the background material and literature review for supervised
sequence labelling. Section 2.1 briefly reviews supervised learning in general. Section 2.2 …

We have compared the performance of four approaches for automatic language
identification of speech utterances: Gaussian mixture model (GMM) classification; single …

Most current speech recognizers use an observation space based on a temporal sequence
of measurements extracted from fixed-length “frames”(eg, Mel-cepstra). Given a hypothetical …

The acoustic-phonetic modeling component of most current speech recognition systems
calculates a small set of homogeneous frame-based measurements at a single, fixed time …

A new quantitative model of tonal perception for continuous speech is described. The paper
illustrates its ability for automatic stylization of pitch contors, with applicatios to prosodic …

In the information age, computer applications have become part of modern life and this has
in turn encouraged the expectations of friendly interaction with them. Speech, as “the” …

Most current speech recognizers use an observation space which is based on a temporal
sequence of" frames"(eg Mel-cepstra). There is another class of recognizer which further …

The use of exemplar-based methods, such as support vector machines (SVMs), k-nearest
neighbors (kNNs) and sparse representations (SRs), in speech recognition has thus far …

One requirement for researching and building spoken language systems is the availability of
speech data that have been labeled and time-aligned at the phonetic level. Although …

This paper presents new methods for training large neural networks for phoneme probability
estimation. A combination of the time-delay architecture and the recurrent network …