1 edition of Automatic classification of speech recognition hypotheses using acoustic and pragmatic features found in the catalog.
Automatic classification of speech recognition hypotheses using acoustic and pragmatic features
|Series||Saarbrücken dissertations in computational linguistics and language technology -- 19|
|Contributions||Deutsches Forschungszentrum für Künstliche Intelligenz, Universität des Saarlandes. Philosophischen Fakultäten|
|LC Classifications||MLCS 2010/42341 (T)|
|The Physical Object|
|Pagination||228 p. ;|
|Number of Pages||228|
|LC Control Number||2008447070|
The papers present current research in the area of computer speech processing including audio signal processing, automatic speech recognition, speaker recognition, computational paralinguistics, speech synthesis, sign language and multimodal processing, and speech and language resources. Speech Recognition Using Artificial Neural Network – A Review. Bhushan C. Kamble. 1. Abstract--Speech is the most efficient mode of communication between peoples. This, being the best way of communication, could also be a useful. interface to communicate with machines. Therefore the popularity of automatic speech recognition system has been.
Early attempts to design systems for automatic speech recognition were mostly guided by the theory of acoustic-phonetics, which describes the phonetic elements of speech (the basic sounds of the language) and tries to explain how they are acoustically realized in a spoken utterance. These elements include the phonemes and the corresponding. In this paper, we discuss an automatic event-based recognition system (EBS) that is based on phonetic feature theory and acoustic phonetics. First, acoustic events related to the manner phonetic features are extracted from the speech signal. Second, based on the manner acoustic events, information related to the place phonetic features and.
Prosodic classification of dialog acts KEY WORDS ABSTRACT automatic dialog act Identifying whether an utterance is a statement, question, greeting, and so classification forth is integral to effective automatic understanding ofnatural dialog. Little is known, however, about how such dialog acts (DAs) can be automatically discourse modeling classified in truly natural conversation. That is why, automatic speech recognition has gained a lot of popularity. Many approaches for speech recognition exist like Dynamic Time Warping (DTW), Hidden Markov Model (HMM). This paper shows how Neural Network (NN) can be used for speech recognition and also investigates its performance in speech recognition.
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Automatic speech recognition (ASR) has been extensively studied during the past few decades. Today, most of the ASR system based on statistical modelling. In this current study, we presented an automatic speech emotion recognition (SER) system using three machine learning algorithms (MLR, SVM, and RNN) to classify seven emotions.
Thus, two types of features (MFCC and MS) were extracted from two different acted databases (Berlin and Spanish databases), and a combination of these features was Cited by: 1.
Automatic speech recognition (ASR) is an independent, machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker through a.
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.
Techniques in Speech Acoustics provides an introduction to the acoustic analysis and characteristics of speech sounds. The first part of the book covers aspects of the source-filter decomposition of speech, spectrographic analysis, the acoustic theory of speech production and acoustic phonetic cues.
The second part is based on computational techniques for analysing the acoustic speech signal /5(2). Features are disclosed for automatically identifying a speaker.
Artifacts of automatic speech recognition (“ASR”) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance.
The scores can be based on, e.g., individual components of Gaussian mixture. Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers.
It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT).It incorporates knowledge and research in the computer. In this article, the acoustic-phonetic characteristics of the American English fricative consonants are investigated from the automatic classification standpoint.
The features studied in the literature are evaluated and new features are proposed. To test the value of the extracted features, a statistically guided, knowledge-based, acoustic-phonetic system for the automatic classification of.
The paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-three acoustic features are extracted from the speech input.
and design and implementation of speech recognition systems, right from isolated word recognition to large vocabulary continuous speech recognition systems. Neural networks and their use in speech recognition is also presented, though somewhat briefly. Rabiner was the author of the first widely-read tutorial on HMMs, so naturally the Reviews: Speech recognition can be considered a specific use case of the acoustic channel.
The car is a challenging environment to deploy speech recognition. A well-developed speech recognition system should cope with the noise coming from the car, the road, and the entertainment system, and include the following characteristics (Baeyens and Murakami, ). Automatic speech recognition (ASR) systems are finding increasing use in everyday life.
Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street.
This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. Typically, speech understanding systems, such as the Verbmobil speech-to-speech translation system, first use a word recognizer to determine word hypotheses, only based on acoustic and language.
Numerous attempts have been made to find low-dimensional, formant-related representations of speech signals that are suitable for automatic speech recognition. However, it is often not known how these features behave in comparison with true formants.
The purpose of this study was to compare two sets of automatically extracted formant-like features, i.e., robust formants and HMM2 features, to.
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Aggarwal & A. Kumar Journal of Intelligent Systems 30 (1) (). Fundamentals of Speech Recognition (M.S./Ph.D. course) Research Experience: MS State (present): large vocabulary speech recognition, object-oriented DSP Texas Instruments (): telephone-based speech recognition, Tsukuba R&D Center AT&T Bell Laboratories (): isolated word speech recognition, low-rate speech coding.
This paper describes a novel application of multiresolution analysis (MRA) in extracting acoustic features that possess de-noising capability for robust speech recognition. The MRA algorithm is used to construct a mel-scaled wavelet packet filter-bank, from which subband powers are computed as the feature parameters for speech recognition.
Wiener filtering is applied to a few selected. This book discusses large margin and kernel methods for speech and speaker recognition. Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition.
It presents theoretical and practical foundations of these methods, from support vector. Automatic Speech Recognition system classification: The following tree structure emphasizes the speech processing applications. Depending on the chosen criterion, Automatic Speech Recognition systems can be classified as shown in figure 2.
Relevant issues of ASR design: Main issues on which recognition accuracy depends have been. Automatic Classification of Speech Recognition Hypotheses Using Acoustic and Pragmatic Features Malte Gabsdil Universität des Saarlandes.Speech Recognition and Understanding • Recognition and Understanding of Speechis the process of extracting usable linguistic information from a speech signal in support of human-machine communication by voice – command and control (C&C) applications, e.g., simple commands for spreadsheets, presentation graphics, appliances.K.
Sreenivasa Rao, S.G. Koolagudi, Recognition of emotions from video using acoustic and facial features, in Signal, Image and Video Processing (SIViP), pp. 1–17 () Google Scholar S.J. Young, The general use of tying in phone-based hmm speech recognizers, in IEEE International Conference on Acoustics, Speech, and Signal Processing.