m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on

Prosodic phrase structure

Abstract not found

This paper presents an algorithm for automatically assigning phrase breaks to unrestricted text for use in a text-to-speech synthesizer. Text is first converted into a sequence of part-of-speech tags. Next a Markov model is used to give the most likely sequence of phrase breaks for the input part-of-speech tags. In the Markov model, states represent types of phrase break and the transitions between states represent the likelihoods of sequences of phrase types occurring. The paper reports a variety of experiments investigating part-of-speech tag-sets, Markov model structure and smoothing. The best setup correctly identifies 79 % of breaks in the test corpus. © 1998 Academic Press Limited 1.

Interpreting fully natural speech is an important goal for spoken language understanding systems. However, while corpus studies have shown that about 10% of spontaneous utterances contain self-corrections, or PEPAIRS, little is known about the extent to which cues in the speech signal may facilitate repair processing. We identify several cues based on acoustic and prosodic analysis of repairs in a corpus of spontaneous speech, and propose methods for exploiting these cues to detect and correct repairs. We test our acoustic-prosodic cues with other lexical cues to repair identification and find that precision rates of 89-93% and recall of 78-83% can be achieved, depending upon the cues employed, from a prosodically labeled corpus.

d in these algorithms is that the inputs to the phrase break assignment algorithm have to be available at phrase break assignment time, and themselves be predictable from raw text. For example, some algorithms require accent assignment information but we believe accent assignment can only take place after prosodic boundaries are identified. A second example is the requirement of syntactic parsing of the input without providing a syntactic parser to achieve this. Thus we have ensured that both our phrase break assignment algorithm is properly placed within a full text to speech system and that the prediction of any required inputs is included in our tests. A second requirement for our algorithm was introduced by our observation that many phrase break assignment algorithms attempt to estimate the probability of a break at some point based only on local information. However, what may locally appear as a reasonable position for a break may in fact be less suitable than the position after t

With the development of speech recognition and synthesis technology, speech interfaces for practical applications are in high demand. For applications like spoken dialogues systems, where not only the waveform but also the content of a system’s query/response have to be generated automatically, a Concept-to-Speech system is needed. One key module in a Concept-to-Speech system is prosody modeling. It determines how prosody (intonation), the suprasegmental aspect of speech that communicates the structure and meaning of utterances, should be represented and generated automatically. Since prosody directly affected by the meaning and structure of the sentences automatically produced by a natural language generator; at the same time, it also has significant influence on the naturalness and effectiveness of the speech synthesized, its performance is critical to the success of a Conceptto-Speech system where both natural language generation and speech synthesis are used together to generate the final spoken output. In this thesis, I focus on two aspects of the prosody modeling process. First, I explore novel features that are available during natural language generation, such as the meaning, structure, and context of sentences, and demonstrate how these features are related to prosody, based on empirical evidences derived from annotated speech corpora. Second, I propose a new prosody modeling approach that automatically combines different natural language features for prosody prediction. More specifically, I designed an augmented instance-based learning algorithm that makes use of the natural prosody in human speech to produce natural and vivid synthesized speech. Our subjective evaluation demonstrates the effectiveness of this approach. I implement the prosody modeling system for a medical application called MAGIC.

Concept To Speech (CTS) systems are closely related to two other types of systems: Natural Language Generation (NLG) and Speech Synthesis (SS). In this paper, we propose a new architecture for a CTS system. A Speech Integrating Markup Language (SIML) is designed as an general interface for integrating NLG and SS. We also present a CTS system for a multimedia presentation generation application. We discuss how to extend the current CTS sys- tem based on the new architecture. Cur- rently, only limited semantic, syntactic and prosodic features are covered inour proto- type system.

This thesis proposes a phonetic model of English intonation which is a system for linking the phonological and F 0 descriptions of an utterance. It is argued that such a model should take the form of a rigorously defined formal system which does not require any human intuition or expertise to operate. It is also argued that this model should be capable of both analysis (F 0 to phonology) and synthesis (phonology to F 0 ). Existing phonetic models are reviewed and it is shown that none meet the specification for the type of formal model required. A new phonetic model is presented that has three levels of description: the F 0 level, the intermediate level and the phonological level. The intermediate level uses the three basic elements of rise, fall and connection to model F 0 contours. A mathematical equation is specified for each of these elements so that a continuous F 0 contour can be created from a sequence of elements. The phonological system uses H and L to describe high and low pi...

In this paper, we report on an effort to provide a general-purpose spoken language generation tool for Concept-to-Speech (CTS) applications by extending a widely used text generation package, FUF/SURGE, with an intonation generation component. As a first step, we applied machine learning and statistical models to learn intonation rules based on the semantic and syntactic information typically represented in FUF/SURGE at the sentence level. The results of this study are a set of intonation rules learned automatically which can be directly implemented in our intonation generation component. Through 5-fold cross-validation, we show that the learned rules achieve around 90% accuracy for break index, boundary tone and phrase accent and 80% accuracy for pitch accent. Our study is unique in its use of features produced by language generation to control intonation. The methodology adopted here can be employed directly when more discourse/pragmatic information is to be considered in the future.