There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Principles of Language Learning and Teaching

The Transmission Control Protocol.

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 turn taking, 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 a set of recommendations for enabling the development of the next generation of socially aware computing.

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Social Signal Processing

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

ICSLP2000: the 6th International Conference on Spoken Language Processing, October 16-20, 2000, Beijing, China.

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Free software toolkit for Japanese large vocabulary continuous speech recognition

This paper describes a method to detect smiles and laughter sounds from the video of natural dialogue. A smile is the most common facial expression observed in a dialogue. Detecting a user's smiles and laughter sounds can be useful for estimating the mental state of the user of a spoken-dialogue-based user interface. In addition, detecting laughter sound can be utilized to prevent the speech recognizer from wrongly recognizing the laughter sound as meaningful words. In this paper, a method to detect smile expression and laughter sound robustly by combining an image-based facial expression recognition method and an audio-based laughter sound recognition method. The image-based method uses a feature vector based on feature point detection from face images. The method could detect smile faces by more than 80% recall and precision rate. A method to combine a GMM-based laughter sound recognizer and the image-based method could improve the accuracy of detection of laughter sounds compared with methods that use image or sound only. As a result, more than 70% recall and precision rate of laughter sound detection was obtained from the natural conversation videos

Smile and laughter recognition using speech processing and face recognition from conversation video

Speech recognition by a small robot is difficult because the robot makes noise itself. In this paper, two new methods are proposed that suppresses internal noise of the small robots. These methods are based on spectral subtraction (SS). The difference of the proposed methods from the original SS is that the proposed methods use the estimated noise spectrum dependent on the motion of the robot. One method, called MDSS, prepares the noise spectrums for all motions. Another method, called NPSS, predicts the noise spectrum from angular velocities of all joints of the robot using a neural network. From the results of the comparison between the original SS and the proposed methods, the proposed methods outperformed the conventional SS. The NPSS worked well even when the noise of the motion was unstable, while the MDSS method gave good result when the noise in one motion was stable.

Internal Noise Suppression for Speech Recognition by Small Robots

Recently, several music information retrieval (MIR) systems have been developed which retrieve musical pieces by the user’s singing voice. All of these systems use only the melody information for retrieval. Although the lyrics information is useful for retrieval, there have been few attempts to exploit lyrics in the user’s input. In order to develop a MIR system that uses lyrics and melody information, lyrics recognition is needed. Lyrics recognition from a singing voice is achieved by similar technology to that of speech recognition. The difference between lyrics recognition and general speech recognition is that the input lyrics are a part of the lyrics of songs in a database. To exploit linguistic constraints maximally, we described the recognition grammar using a finite state automaton (FSA) that accepts only lyrics in the database. In addition, we carried out a “singing voice adaptation” using a speaker adaptation technique. In our experimental results, about 86% retrieval accuracy was obtained.

/pdf/lyrics-recognition-from-a-singing-voice-based-on-finite-2bo0xek2ec.pdf

Lyrics recognition from a singing voice based on finite state automaton for music information retrieval

The Japanese Dictation Toolkit has been designed and developed as a baseline platform for Japanese LVCSR (Large Vocabulary Continuous Speech Recognition). The platform consists of a standard recognition engine, Japanese phone models and Japanese statistical language models. We set up a variety of Japanese phone HMMs from a contextindependent monophone to a triphone model of thousands of states. They are trained with ASJ (The Acoustical Society of Japan) databases. A lexicon and word N-gram (2-gram and 3-gram) models are constructed with a corpus of Mainichi newspaper. The recognition engine JULIUS is developed for evaluation of both acoustic and language models. As an integrated system of these modules, we have implemented a baseline 5, 000-word dictation system and evaluated various components. The software repository is available to the public.

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Akinori Ito

Papers

Free software toolkit for Japanese large vocabulary continuous speech recognition

Smile and laughter recognition using speech processing and face recognition from conversation video

Internal Noise Suppression for Speech Recognition by Small Robots

Lyrics recognition from a singing voice based on finite state automaton for music information retrieval

Japanese Dictation Toolkit -1997 version