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Migrations in Speech Recognition
Language and Cognitive Processes, 1996A new paradigm that may be appropriate for uncovering speech perceptual codes is described. Illusory words (e.g. /bi u/) are detected by blending two dichotic stimuli (e.g. /bit -k u/). According to the logic of illusory conjunctions (Treisman & Schmidt, 1982), the speech unit involved in such illusions must be separately registered as an independent ...
Kolinsky, Régine, Morais, Jose
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"Unvoiced speech recognition using EMG - mime speech recognition"
CHI '03 extended abstracts on Human factors in computing systems - CHI '03, 2003We propose unvoiced speech recognition, "Mime Speech Recognition". It recognizes speech by observing the muscles associated with speech. It is not based on voice signals but electromyography (EMG). It will realize unvoiced communication, which is a new communication style.
Hiroyuki Manabe +2 more
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2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2009
This Plenary presents automatic speech recognition (ASR) as a task of artificial intelligence. The basis, the methodology, spectral processing, distance measures for speech, segmentation speech, spectral and temporal variability, application of Markov Models, noise robustness, Language Models for ASR, are presented.
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This Plenary presents automatic speech recognition (ASR) as a task of artificial intelligence. The basis, the methodology, spectral processing, distance measures for speech, segmentation speech, spectral and temporal variability, application of Markov Models, noise robustness, Language Models for ASR, are presented.
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Speech segmentation without speech recognition
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003In this paper, we presented a semantic speech segmentation approach, in particular sentence segmentation, without speech recognition. In order to get phoneme level information without word recognition information, a novel vowel/consonant/pause (V/C/P) classification is proposed.
Hong-Jiang Zhang, Dong Wang, Lie Lu
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2008
This chapter gives an overview of the main architectures for enabling speech recognition on embedded devices. Starting with a short overview of speech recognition; an overview of the main challenges for the use on embedded devices is given. Each of the architectures has its own characteristic problems and features.
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This chapter gives an overview of the main architectures for enabling speech recognition on embedded devices. Starting with a short overview of speech recognition; an overview of the main challenges for the use on embedded devices is given. Each of the architectures has its own characteristic problems and features.
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A Neural Ensemble For Speech Recognition
1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96, 2005Automatic speech recognition is a severe application for neural networks. An ensemble of more neural networks can be the keystone to increase the performance of the recognizer. Different techniques to pre-process the vocal signal are also shown. This approach is used to implement a “viva voice” recognizer for a car phone.
M. Costa +3 more
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Microphones for speech and speech recognition
The Journal of the Acoustical Society of America, 2004Automatic speech recognition (ASR) requires about a 15- to 20-dB signal-to-noise ratio (S/N) for high accuracy even for small vocabulary systems. This S/N is generally achievable using a telephone handset in normal office or home environments. In the early 1990s ATT and the regional telephone companies began using speaker-independent ASR to replace ...
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Speech recognition and speech synthesis
Euromicro Newsletter, 1979Abstract Speech synthesis and speech recognition are still in the experimental stage. Much remains to be done in this field, but looking at the ever growing amount of people on this subject the “pergect” speech synthesizer featuring low cost and high performance is to be expected soon.
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The Journal of the Acoustical Society of America, 1990
Smoothed frame labeling associates phonetic frame labels with a given speech frame as a function of (a) the closeness with which the given frame compares to each of a plurality of acoustic models, (b) which frame labels correspond with a neighboring frame, and (c) transition probabilities which indicate, for the frame labels associated with the ...
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Smoothed frame labeling associates phonetic frame labels with a given speech frame as a function of (a) the closeness with which the given frame compares to each of a plurality of acoustic models, (b) which frame labels correspond with a neighboring frame, and (c) transition probabilities which indicate, for the frame labels associated with the ...
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Speech recognition based on speech units
European Conference on Speech Technology, 1987In a classical quantization system, each vector is represented by the nearest centroid; two vectors belonging to the same class are then indistinguishable. In order to mitigate this situation, we take into account the two nearest neighbours and define a “belonging degree” calculated from the distances between the vector and the two centroids.
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