• Corpus ID: 5960831

Theory and Application of Digital Speech Processing by

@inproceedings{RabinerTheoryAA,
  title={Theory and Application of Digital Speech Processing by},
  author={Lawrence R. Rabiner and Ronald W. Schafer}
}
Schafer, and is made available only for the educational use of their students and students in Course 6.341 at MIT. It is for personal use only and it is not to be duplicated, posted on unrestricted websites, or otherwise distributed. 
Introduction to Digital Speech Processing
TLDR
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The proposed system, Zero-Crossing rate and short term energy are used in a fuzzy logic control this classification of speech and achieves 2.5 % error between human classification and automatic classification using this method.
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TLDR
A Zero frequency filtering (ZFF) based new system to stream pitch of multiple concurrent speakers using a workflow to estimate pitch values of all sources in each single frame then streaming them into trajectories, each corresponding to a distinct source.
Dictionary Learning-Based Speech Enhancement
This chapter presents an overview of dictionary learning-based speech enhancement methods. Specifically, we review the existing algorithms that employ sparse representation (SR), nonnegative matrix
Comparison between Speech Parameters for Forensic Voice Comparison Using Mobile Phone Speech
Amongst the various speech parameters available for forensic voice comparison (FVC), Mel-frequency cepstral coefficients (MFCCs) have been found to give good performance and are widely used. The
BaNa: A hybrid approach for noise resilient pitch detection
TLDR
This paper presents a hybrid noise resilient pitch detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis and shows that for all types of noises and SNR values investigated, BaNa achieves the best pitch detection accuracy.
Procedure for Cepstral Analysis in tracing unique voice segments
TLDR
In basic spectrum analysis the range of frequency specific to a particular voice sample is obtained where as the team decided to device a unique pitch bifurcation method by Cepstral analysis.
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References

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Advances in Speech Signal Processing
In 25 original chapter-articles, leading authorities address various aspects of speech signal processing, stressing the advances during the past five to ten years. The volume presents a wealth of
Digital processing of speech signals
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Digital Speech: Coding for Low Bit Rate Communication Systems
TLDR
A detailed account of the most recently developed digital speech coders designed specifically for use in the evolving communications systems, including an in-depth examination of the important topic of code excited linear prediction (CELP).
Applications of digital signal processing to audio and acoustics
TLDR
There are whole classes of algorithms that the speech community is not interested in pursuing or using in digital signal processing of sound and these algorithms and techniques are revealed in this book.
Speech Analysis, Synthesis and Perception
TLDR
A second edition was begun in 1970, the aim was to retain the original format, but to expand the content, especially in the areas of digital communications and com puter techniques for speech signal processing.
Fundamentals of speech recognition
TLDR
This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
Computers that talk and listen: Man-machine communication by voice
TLDR
Computer techniques now emerging in the laboratory promise new capabilities for voice communication between man and machine that extend to: voice-directed installation of telephone equipment, authentication by voice of a credit customer or of an individual requesting readout of privileged information, and voice-controlled services such as repertory dialing or automatic booking of travel reservations.
Discrete-Time Processing of Speech Signals
TLDR
The preface to the IEEE Edition explains the background to speech production, coding, and quality assessment and introduces the Hidden Markov Model, the Artificial Neural Network, and Speech Enhancement.
Text-to-Speech Synthesis
TLDR
Text-to-Speech Synthesis provides an in-depth explanation of all aspects of current speech synthesis technology, and is designed for graduate students in electrical engineering, computer science, and linguistics.
Speech Processing
TLDR
Analysis of discrete-time speech signals probability and random processes linear model and dynamic system model optimization methods and estimation theory statistical pattern recognition helps clarify speech technology in selected areas.
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