2,507 research outputs found
Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean
A new tightly coupled speech and natural language integration model is
presented for a TDNN-based continuous possibly large vocabulary speech
recognition system for Korean. Unlike popular n-best techniques developed for
integrating mainly HMM-based speech recognition and natural language processing
in a {\em word level}, which is obviously inadequate for morphologically
complex agglutinative languages, our model constructs a spoken language system
based on a {\em morpheme-level} speech and language integration. With this
integration scheme, the spoken Korean processing engine (SKOPE) is designed and
implemented using a TDNN-based diphone recognition module integrated with a
Viterbi-based lexical decoding and symbolic phonological/morphological
co-analysis. Our experiment results show that the speaker-dependent continuous
{\em eojeol} (Korean word) recognition and integrated morphological analysis
can be achieved with over 80.6% success rate directly from speech inputs for
the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer
processing of oriental language journa
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals
Human infants can discover words directly from unsegmented speech signals
without any explicitly labeled data. In this paper, we develop a novel machine
learning method called nonparametric Bayesian double articulation analyzer
(NPB-DAA) that can directly acquire language and acoustic models from observed
continuous speech signals. For this purpose, we propose an integrative
generative model that combines a language model and an acoustic model into a
single generative model called the "hierarchical Dirichlet process hidden
language model" (HDP-HLM). The HDP-HLM is obtained by extending the
hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by
Johnson et al. An inference procedure for the HDP-HLM is derived using the
blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure
enables the simultaneous and direct inference of language and acoustic models
from continuous speech signals. Based on the HDP-HLM and its inference
procedure, we developed a novel double articulation analyzer. By assuming
HDP-HLM as a generative model of observed time series data, and by inferring
latent variables of the model, the method can analyze latent double
articulation structure, i.e., hierarchically organized latent words and
phonemes, of the data in an unsupervised manner. The novel unsupervised double
articulation analyzer is called NPB-DAA.
The NPB-DAA can automatically estimate double articulation structure embedded
in speech signals. We also carried out two evaluation experiments using
synthetic data and actual human continuous speech signals representing Japanese
vowel sequences. In the word acquisition and phoneme categorization tasks, the
NPB-DAA outperformed a conventional double articulation analyzer (DAA) and
baseline automatic speech recognition system whose acoustic model was trained
in a supervised manner.Comment: 15 pages, 7 figures, Draft submitted to IEEE Transactions on
Autonomous Mental Development (TAMD
Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling
In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20\% relative is possible
SVMs for Automatic Speech Recognition: a Survey
Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact.
During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed.
These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research
Visual units and confusion modelling for automatic lip-reading
Automatic lip-reading (ALR) is a challenging task because the visual speech signal is known to be missing some important information, such as voicing. We propose an approach to ALR that acknowledges that this information is missing but assumes that it is substituted or deleted in a systematic way that can be modelled. We describe a system that learns such a model and then incorporates it into decoding, which is realised as a cascade of weighted finite-state transducers. Our results show a small but statistically significant improvement in recognition accuracy. We also investigate the issue of suitable visual units for ALR, and show that visemes are sub-optimal, not but because they introduce lexical ambiguity, but because the reduction in modelling units entailed by their use reduces accuracy
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