Results 281 to 290 of about 46,840 (304)

Speech recognition with localized time-frequency pattern detectors

2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU), 2007
A method for acoustic modeling of speech is presented which is based on learning and detecting the occurrence of localized time-frequency patterns in a spectrogram. A boosting algorithm is applied to both build classifiers and perform feature selection from a large set of features derived by filtering spectrograms.
Ken Schutte, James R. Glass
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Pattern Recognition for Speech Detection

2013
The supervised pattern recognition techniques such as back-propagation neural network (BPNN), support vector machine (SVM), hidden Markov model (HMM), and Gaussian mixture model (GMM) that are used to design the classifier for speech and speaker detection are described in this chapter.
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Keynote speakers: The challenges of pattern recognition for speech signals

2014 IEEE 12th International New Circuits and Systems Conference (NEWCAS), 2014
Speech coding has found great success in today's widespread usage of cellphones. In addition, people are increasingly accustomed to hearing and accepting synthetic voices when they access information by phone. A third major system used for speech, automatic speech recognition (ASR), is also seeing significant usage, but still has major limitations ...
Douglas D. O'Shaughnessy, Géza Kolumbán, Roger Lecomte   +2 more
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Natural behavioral patterns of speech recognition error recovery

2010 Sixth International Conference on Natural Computation, 2010
Due to faster entry speed and lower attention demand, speech recognition is widely used in the multimodal text entry system. But error recovery is a big challenge to its usability for the accuracy of speech recognition is not good enough. However, few studies focused on the question how users recovered the errors in a multimodal entry system ...
Licheng Xue, Xianghong Sun
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A pattern recognition approach to compare natural and synthesized speech

ICASSP '79. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
This paper presents some possible acoustic feature differences between natural and synthesized speech. Sentences spoken in a natural adult male voice and synthesized on VOTRAX ML-1 Speech Synthesizer were recorded in a sound proof booth. The recorded sentences were classified into voiced, unvoiced and silence regions contained in these sentences ...
S. Sheshadri, Manjula B. Waldron
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Bionics Approach to Speech-Pattern Recognition

The Journal of the Acoustical Society of America, 1966
Research on speech-pattern recognition has been done using an electronic model of inner ear and nervous-system functions (Analog Cochlea and Middle C). Pulse patterns as they might occur in the nervous system at the level of primary auditory neurons can be sampled and displayed by the real-time digital data-processing system. The analog “auditory nerve”
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On the role of binary mask pattern in automatic speech recognition

Interspeech 2012, 2012
Processing noisy signals using the ideal binary mask has been shown to improve automatic speech recognition (ASR) performance. In this paper, we present the first study that investigates the role of mask patterns in ASR under varying signalto-noise ratios (SNR), noise conditions and mask definitions.
Arun Narayanan, DeLiang Wang
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Speech recognition for recognizing the category of an input speech pattern

The Journal of the Acoustical Society of America, 1991
Speech recognition method and system are adapted to previously prepare a noise pattern in response to environmental noise prior to inputting a speech signal, evaluate a speech feature vector Bi yielded by subtracting the noise pattern from a feature vector Ai of the input speech upon inputting the speech signal thereafter, spectrum-normalize the speech
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Research on Isolated Word Speech Recognition Based on Biomimetic Pattern Recognition

2009 International Conference on Artificial Intelligence and Computational Intelligence, 2009
In this paper, the theories of biomimetic pattern recognition and high-dimension space covering are applied into the isolated word speech recognition. And, based on Hopfield network and RBF network, a new type of neural network model is constructed to realize the coverage of different types of samples which form different geometrical shapes in high ...
Bin Lu, Jing-jing Xu
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Pattern Recognition in Speech and Language Processing

2003
Minimum Classification Error (MSE) Approach in Pattern Recognition, Wu Chou Minimum Bayes-Risk Methods in Automatic Speech Recognition, Vaibhava Goel and William Byrne A Decision Theoretic Formulation for Adaptive and Robust Automatic Speech Recognition, Qiang Huo Speech Pattern Recognition Using Neural Networks, Shigeru Katagiri Large Vocabulary ...
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