Abstract not found

Research on speech and emotion is moving from a period of exploratory research into one where there is a prospect of substantial applications, notably in human–computer interaction. Progress in the area relies heavily on the development of appropriate databases. This paper addresses four main issues that need to be considered in developing databases of emotional speech: scope, naturalness, context and descriptors. The state of the art is reviewed. A good deal has been done to address the key issues, but there is still a long way to go. The paper shows how the challenge of developing appropriate databases is being addressed in three major recent projects––the Reading–Leeds project, the Belfast project and the CREST–ESPproject. From these and other studies the paper draws together the tools and methods that have been developed, addresses the problems that arise and indicates the future directions for the development of emotional speech databases.

The proposal examined here is that speakers use uh and um to announce that they are initiating what they expect to be a minor (uh), or major (um), delay in speaking. Speakers can use these announcements in turn to implicate, for example, that they are searching for a word, are deciding what to say next, want to keep the floor, or want to cede the floor. Evidence for the proposal comes from several large corpora of spontaneous speech. The evidence shows that speakers monitor their speech plans for upcoming delays worthy of comment. When they discover such a delay, they formulate where and how to suspend speaking, which item to produce (uh or um), whether to attach it as a clitic onto the previous word (as in "and-uh"), and whether to prolong it. The argument is that uh and um are conventional English words, and speakers plan for, formulate, and produce them just as they would any word.

Infants show evidence of phonetic categorization and of perceptual parsing of the speech stream before they learn to speak, before they have large vocabularies, and possibly before they even understand that words are referential. Some parts of the speech processing system are initiated early. However, the system takes a long time

This paper reconsiders those assumptions and describes an analysis of phrasing that we believe corrects many of the problems of the earlier version. Like the earlier version, it has been implemented in a text-to-speech system that uses a natural language parser and prosody rules to generate information about the location and relative strength of prosodic phrase boundaries

paper is now badly out of date, but since it is still frequently cited I feel it is worth making it available in electronic form. The main point of the paper is that it is practically impossible to devise an objective test that will distinguish between "stress-timed " and "syllable-timed" languages just on the basis of measuring time intervals).

The numbers In the last issue, we defined a perfect numberN to be one for which the sum of the divisors d (1 ≤ d < N) is N. We gave the first few perfect numbers, starting with those known by the early Greeks. Here we give an extended list, with some comments about their discovery. The early Greeks were aware of the first four perfect numbers:

The term ToBI has come to be used in two different ways. Originally, it was the name of an annotation system, developed in the period 1991 to 1994, which was designed for use in labelling intonation and prosody in databases of spoken Mainstream American English (Beckman and Hirschberg 1994). Very quickly, however, it also came to refer to a general

Social Signal Processing is the research domain aimed at bridging the social intelligence gap between humans and machines. This article is the first survey of the domain that jointly considers its three major aspects, namely modeling, analysis and synthesis of social behaviour. Modeling investigates laws and principles underlying social interaction, analysis explores approaches for automatic understanding of social exchanges recorded with different sensors, and synthesis studies techniques for the generation of social behaviour via various forms of embodiment. For each of the above aspects, the paper includes an extensive survey of the literature, points to the most important publicly available resources, and outlines the most fundamental challenges ahead.

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.