This paper describes MDWOZ, a development environment for spoken dialog systems based on the Wizard of Oz technique, whose main goal is to facilitate data collection (speech signal and dialog related information) and interaction model building. Both these tasks can be quite difficult, and such an environment can facilitate them very much. Due to the modular way in which MDWOZ was implemented, it is possible to reuse parts of it in the final dialog system. The environment provides language-transparent facilities and accessible methods such that even non-computing specialists can participate in spoken dialog systems development. The main features of the environment are presented, together with some test experiments.

/pdf/mdwoz-a-wizard-of-oz-environment-for-dialog-systems-2wztbag53t.pdf

MDWOZ: A Wizard of Oz Environment for Dialog Systems Development

Abstract This paper describes a bi-directional letter/sound generation system based on a strategy combining data-driven techniques with a rule-based formalism. Our approach provides a hierarchical analysis of a word, including stress pattern, morphology and syllabification. Generation is achieved by a probabilistic parsing technique, where probabilities are trained from a parsed lexicon. Our training and testing corpora consisted of spellings and pronunciations for the high frequency portion of the Brown Corpus (10,000 words). The phonetic labels are augmented with markers indicating morphology and stress. We will report on two distinct grammars representing a historical perspective. Our early work with the first grammar inspired us to modify the grammar formalism, leading to greater constraint with fewer rules. We evaluated our performance on letter-to-sound generation in terms of whole word accuracy as well as phoneme accuracy. For the unseen test set, we achieved a word accuracy of 69.3% and a phoneme accuracy of 91.7% using a set of 52 distinct phonemes. While this paper focuses on letter-to-sound generation, our system is also capable of generation in the reverse direction, as reported elsewhere (Meng et al., 1994a). We believe that our formalism will be especially applicable for entering unknown words orally into a recognition system.

Reversible letter-to-sound sound-to-letter generation based on parsing word morphology.

Reversible letter-to-sound/sound-to-letter generation based on parsing word morphology

Publisher Summary This chapter describes speech interfaces in terms of the enhancing properties of the acoustic medium such as the ability to perform tasks requiring divided attention; speech interfaces are also described in terms of shortcomings of speech such as its disruptive, invasive nature. The chapter takes the practitioner's perspective on how to construct applications that use speech as output and begins with a consideration of when to use natural speech and when to use synthetic speech. When the output is relatively unpredictable and unbounded, synthetic speech is the only practical choice. However, hurdles to correct pronunciation arise in most realistic applications. The most frequent hurdle is that most applications that require synthesis also access databases that were never intended to have spoken language output. While speech synthesis can be purchased as a commodity and as a black box technology, it is important to have some overall understanding of the underlying technologies and how synthesis is actually done. There are several choices in how to represent what is to be pronounced: letter-to-sound rules, pronouncing dictionaries, or some hybrid approach. Considerations of each approach are outlined and evaluated in the chapter against technological trends such as increased processor speeds and decreased storage costs.

Applying Speech Synthesis to User Interfaces

In this paper, we describe a reversible letter-to-sound/sound-to-letter generation system based on an approach which combines a rule-based formalism with data-driven techniques. We adopt a probabilistic parsing strategy to provide a hierarchical lexical analysis of a word, including information such as morphology, stress, syllabification, phonemics and graphemics. Long-distance constraints are propagated by enforcing local constraints throughout the hierarchy. Our training and testing corpora are derived from the high-frequency portion of the Brown Corpus (10,000 words), augmented with markers indicating stress and word morphology. We evaluated our performance based on an unseen test set. The percentage of nonparsable words for letter-to-sound and sound-to-letter generation were 6% and 5% respectively. Of the remaining words our system achieved a word accuracy of 71.8% and a phoneme accuracy of 92.5% for letter-to-sound generation, and a word accuracy of 55.8% and letter accuracy of 89.4% for sound-to-letter generation. We also compared our hierarchical approach with an alternative, single-layer approach to demonstrate how the hierarchy provides a parsimonious description for English orthographic-phonological regularities, while simultaneously attaining competitive generation accuracy.

Phonological parsing for bi-directional letter-to-sound/sound-to-letter generation

On the development of a name pronunciation system.

The design of a real world wizard of oz experiment for a speech driven telephone directory information system.

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On the development of a name pronunciation system.

The design of a real world wizard of oz experiment for a speech driven telephone directory information system.