Computer vision seeks a process that starts with a noisy, ambiguous signal from a TV camera and ends with a high-level description of discrete objects located in 3-dimensional space and identified in a human classification. This book addresses the process at several levels. First to be treated are the low-level image-processing issues of noise removal and smoothing while preserving important lines and singularities in an image. At a slightly higher level, a robust contour tracing algorithm is described that produces a cartoon of the important lines in the image. Third is the high-level task of reconstructing the geometry of objects in the scene. The book has two aims: to give the computer vision community a new approach to early visual processing, in the form of image segmentation that incorporates occlusion at a low level, and to introduce real computer algorithms that do a better job than what most vision programmers use currently. The algorithms are: - a nonlinear filter that reduces noise and enhances edges, - an edge detector that also finds corners and produces smoothed contours rather than bitmaps, - an algorithm for filling gaps in contours.

Time-Domain Beamforming and Blind Source Separation (Lecture Notes in Computer Science)

Computers & Technology

Minker, Wolfgang

Bourgeois, Julien

Computer Science

Springer

This book addresses the problem of separating spontaneous multi-party speech by way of microphone arrays (beamformers) and adaptive signal processing techniques. It is written is a concise manner and an effort has been made such that all presented algorithms can be straightforwardly implemented by the reader. All experimental results have been obtained with real in-car microphone recordings involving simultaneous speech of the driver and the co-driver.

Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment (Lecture Notes in Electrical Engineering, 3)

Incorporating Knowledge Sources into Statistical Speech Recognition addresses the problem of developing efficient automatic speech recognition (ASR) systems, which maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training / recognition effort feasible and improving speech recognition performance. The book provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. It can be applied to many existing ASR problems with their respective model-based likelihood functions in flexible ways.

Incorporating Knowledge Sources into Statistical Speech Recognition (Lecture Notes in Electrical Engineering, 42)

Sakti, Sakriani

Markov, Konstantin

Nakamura, Satoshi

Stochastically-Based Semantic Analysis investigates the problem of automatic natural language understanding in a spoken language dialog system. The focus is on the design of a stochastic parser and its evaluation with respect to a conventional rule-based method. Stochastically-Based Semantic Analysis will be of most interest to researchers in artificial intelligence, especially those in natural language processing, computational linguistics, and speech recognition. It will also appeal to practicing engineers who work in the area of interactive speech systems.

Stochastically-Based Semantic Analysis (The Springer International Series in Engineering and Computer Science, 514)

Waibel, Alex

Mariani, Joseph

The authors set out the theory and methods for quality enhancement of clean and distorted speech signals such as those that have undergone a band limitation in a telephone network. Problems and solutions are discussed for the different approaches.

Bandwidth Extension of Speech Signals (Lecture Notes in Electrical Engineering, 13)

Schmidt, Gerhard

Iser, Bernd

Reasoning for Information: Seeking and Planning Dialogues provides a logic-based reasoning component for spoken language dialogue systems. This component, called Problem Assistant is responsible for processing constraints on a possible solution obtained from various sources, namely user and the system's domain-specific information. The authors also present findings on the implementation of a dialogue management interface to the Problem Assistant. The dialogue system supports simple mixed-initiative planning interactions in the TRAINS domain, which is still a relatively complex domain involving a number of logical constraints and relations forming the basis for the collaborative problem-solving behavior that drives the dialogue.

Domain-Level Reasoning for Spoken Dialogue Systems

Bühler, Dirk

Stochastically-Based Semantic Analysis (The Springer International Series in Engineering and Computer Science)

Novel Techniques for Dialectal Arabic Speech Recognition

Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers. In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and Maximum A-Posteriori (MAP) to adapt existing phonemic MSA acoustic models with a small amount of dialectal ECA speech data. Speech recognition results indicate a significant increase in recognition accuracy compared to a baseline model trained with only ECA data.

Elmahdy, Mohamed

Gruhn, Rainer

Towards Adaptive Spoken Dialog Systems

In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and accurate use. Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted recordings several thousand real users from commercial and non-commercial SDS.
Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer more natural and robust interactions. This work provides insights, lessons and inspiration for future research and development, not only for spoken dialog systems, but for data-driven approaches to human-machine interaction in general.

Schmitt, Alexander

See the best price to sell, buy, or rent books by Minker, Wolfgang. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Minker, Wolfgang

See the best price to sell, buy, or rent books by Minker, Wolfgang. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Time-Domain Beamforming and Blind Source Separation (Lecture Notes in Computer Science)

Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment (Lecture Notes in Electrical Engineering, 3)

Incorporating Knowledge Sources into Statistical Speech Recognition (Lecture Notes in Electrical Engineering, 42)

Stochastically-Based Semantic Analysis (The Springer International Series in Engineering and Computer Science, 514)

Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment (Lecture Notes in Electrical Engineering, 3)

Bandwidth Extension of Speech Signals (Lecture Notes in Electrical Engineering, 13)

Domain-Level Reasoning for Spoken Dialogue Systems

Stochastically-Based Semantic Analysis (The Springer International Series in Engineering and Computer Science)

Novel Techniques for Dialectal Arabic Speech Recognition

Towards Adaptive Spoken Dialog Systems