Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition.
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài:
Research Article Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Compensating Acoustic Mismatch Using Class-Based Histogram Equalization for Robust Speech Recognition
We demonstrate that transformation-based learning can be used to correct noisy speech recognition transcripts in the lecture domain with an average word error rate reduction of 12.9%. Our method is distinguished from earlier related work by its robustness to small amounts of training data, and its resulting efﬁciency, in spite of its use of true word error rate computations as a rule scoring function.
Zigital speech processing is a maCor field in current research all oAer the world. -n particular
for automatic speech recognition [8?O\, Aery significant achieAements haAe been made since
the first attempts of digit recogni]ers in the YU^G_s and YUXG_s when spectral resonances were
determined by analogue filters and logical circuits.
This section addresses the inverse problem in robust speech processing. A problem that speaker and
speech recognition systems regularly encounter in the commercialized applications is the dramatic
degradation of performance due to the mismatch of the training and operating environments.
In this study, a novel approach to robust dialogue act detection for error-prone speech recognition in a spoken dialogue system is proposed. First, partial sentence trees are proposed to represent a speech recognition output sentence. Semantic information and the derivation rules of the partial sentence trees are extracted and used to model the relationship between the dialogue acts and the derivation rules.
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An FFT-Based Companding Front End for Noise-Robust Automatic Speech Recognition
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Robust Distant Speech Recognition by Combining Multiple Microphone-Array Processing with Position-Dependent CMN
Speech recognition affords automobile drivers a hands-free, eyes-free method of replying to Short Message Service (SMS) text messages. Although a voice search approach based on template matching has been shown to be more robust to the challenging acoustic environment of automobiles than using dictation, users may have difficulties verifying whether SMS response templates match their intended meaning, especially while driving. Using a high-fidelity driving simulator, we compared dictation for SMS replies versus voice search in increasingly difficult driving conditions. ...
In this work, we present an experimental analysis of a Dialogue System for the automatization of simple telephone services. Starting from the evaluation of a preliminar version of the system we 1 conclude the necessity to desing a robust and flexible system suitable to have to have different dialogue control strategies depending on the characteristics of the user and the performance of the speech recognition module. Experimental results following the PARADISE framework show an important improvement both in terms of task success and dialogue cost for the proposed system. ...
This book, “DSP for In-Vehicle and Mobile Systems”, contains a collection
of research papers authored by prominent specialists in the field. It is
dedicated to Professor Fumitada Itakura of Nagoya University. It is offered
as a tribute to his sustained leadership in Digital Signal Processing during a
professional career that spans both industry and academe. In many cases, the
work reported in this volume has directly built upon or been influenced by the
innovative genius of Professor Itakura....
Speech interfaces to question-answering systems offer signiﬁcant potential for ﬁnding information with phones and mobile networked devices. We describe a demonstration of spoken question answering using a commercial dictation engine whose language models we have customized to questions, a Web-based textprediction interface allowing quick correction of errors, and an open-domain question-answering system, AnswerBus, which is freely available on the Web.
This paper describes a system that leads us to believe in the feasibility of constructing natural spoken dialogue systems in task-oriented domains. It specifically addresses the issue of robust interpretation of speech in the presence of recognition errors. Robustness is achieved by a combination of statistical error post-correction, syntactically- and semantically-driven robust parsing, and extensive use of the dialogue context.