A High Quality Text-To-Speech System Composed of Multiple Neural Networks

While neural networks have been employed to handle several different text-to-speech tasks, ours is the first system to use neural networks throughout, for both linguistic and acoustic processing. We divide the text-to-speech task into three subtasks, a linguistic module mapping from text to a linguistic representation, an acoustic module mapping from the linguistic representation to speech, and a video module mapping from the linguistic representation to animated images. The linguistic module employs a letter-to-sound neural network and a postlexical neural network. The acoustic module employs a duration neural network and a phonetic neural network. The visual neural network is employed in parallel to the acoustic module to drive a talking head. The use of neural networks that can be retrained on the characteristics of different voices and languages affords our system a degree of adaptability and naturalness heretofore unavailable.Comment: Source link (9812006.tar.gz) contains: 1 PostScript file (4 pages) and 3 WAV audio files. If your system does not support Windows WAV files, try a tool like "sox" to translate the audio into a format of your choic

Do not forget: Full memory in memory-based learning of word pronunciation

Memory-based learning, keeping full memory of learning material, appears a viable approach to learning NLP tasks, and is often superior in generalisation accuracy to eager learning approaches that abstract from learning material. Here we investigate three partial memory-based learning approaches which remove from memory specific task instance types estimated to be exceptional. The three approaches each implement one heuristic function for estimating exceptionality of instance types: (i) typicality, (ii) class prediction strength, and (iii) friendly-neighbourhood size. Experiments are performed with the memory-based learning algorithm IB1-IG trained on English word pronunciation. We find that removing instance types with low prediction strength (ii) is the only tested method which does not seriously harm generalisation accuracy. We conclude that keeping full memory of types rather than tokens, and excluding minority ambiguities appear to be the only performance-preserving optimisations of memory-based learning.Comment: uses conll98, epsf, and ipamacs (WSU IPA

Morphological Analysis as Classification: an Inductive-Learning Approach

Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast we present an inductive learning approach in which morphological analysis is reformulated as a segmentation task. We report on a number of experiments in which five inductive learning algorithms are applied to three variations of the task of morphological analysis. Results show (i) that the generalisation performance of the algorithms is good, and (ii) that the lazy learning algorithm IB1-IG performs best on all three tasks. We conclude that lazy learning of morphological analysis as a classification task is indeed a viable approach; moreover, it has the strong advantages over the traditional approach of avoiding the knowledge-acquisition bottleneck, being fast and deterministic in learning and processing, and being language-independent.Comment: 11 pages, 5 encapsulated postscript figures, uses non-standard NeMLaP proceedings style nemlap.sty; inputs ipamacs (international phonetic alphabet) and epsf macro

Meta-Learning for Phonemic Annotation of Corpora

We apply rule induction, classifier combination and meta-learning (stacked classifiers) to the problem of bootstrapping high accuracy automatic annotation of corpora with pronunciation information. The task we address in this paper consists of generating phonemic representations reflecting the Flemish and Dutch pronunciations of a word on the basis of its orthographic representation (which in turn is based on the actual speech recordings). We compare several possible approaches to achieve the text-to-pronunciation mapping task: memory-based learning, transformation-based learning, rule induction, maximum entropy modeling, combination of classifiers in stacked learning, and stacking of meta-learners. We are interested both in optimal accuracy and in obtaining insight into the linguistic regularities involved. As far as accuracy is concerned, an already high accuracy level (93% for Celex and 86% for Fonilex at word level) for single classifiers is boosted significantly with additional error reductions of 31% and 38% respectively using combination of classifiers, and a further 5% using combination of meta-learners, bringing overall word level accuracy to 96% for the Dutch variant and 92% for the Flemish variant. We also show that the application of machine learning methods indeed leads to increased insight into the linguistic regularities determining the variation between the two pronunciation variants studied.Comment: 8 page

A learning algorithm for multi-layer perceptrons with hard-limiting threshold units

We propose a novel learning algorithm to train networks with multilayer linear-threshold or hard-limiting units. The learning scheme is based on the standard backpropagation, but with "pseudo-gradient" descent, which uses the gradient of a sigmoid function as a heuristic hint in place of that of the hard-limiting function. A justification that the pseudo-gradient always points in the right down hill direction in error surface for networks with one hidden layer is provided. The advantages of such networks are that their internal representations in the hidden layers are clearly interpretable, and well-defined classification rules can be easily obtained, that calculations for classifications after training are very simple, and that they are easily implementable in hardware. Comparative experimental results on several benchmark problems using both the conventional backpropagation networks and our learning scheme for multilayer perceptrons are presented and analyzed