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20171112000053.0 |
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920903s1993 gr r 000 0 eng d |
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|a 0130151572
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|a GR-ChTUC
|b gre
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|a TK7895.S65
|b R33 1993
|a TK7895.S65
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| 082 |
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|2 20
|a 006.4/54
|b R33 1993
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|2 21η
|a 006.454
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|2 20
|a 006.4/54
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| 100 |
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|a Rabiner, Lawrence R.,
|d 1943-
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| 245 |
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|a Fundamentals of speech recognition/
|c Lawrence Rabiner, Biing-HwangJuang
|
| 260 |
1 |
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|a Englewood Cliffs, N.J.:
|b PTR Prentice Hall,
|c c1993
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| 300 |
1 |
0 |
|a xxvii, 507 p. :
|b ill. ;
|c 25 cm.
|
| 490 |
0 |
0 |
|a Prentice Hall signal processing series
|
| 504 |
0 |
0 |
|a Includes bibliographical references and index
|
| 504 |
1 |
0 |
|a Περιέχει βιβλιογραφικές παραπομπές και ευρετήριο
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| 504 |
2 |
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|a Includes bibliographical references and index.
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| 505 |
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|a 1. Fundamentals of Speech Recognition. 1.2. The Paradigm for Speech Recognition. 1.3. Outline. 1.4. A Brief History of Speech-Recognition Research -- 2. The Speech Signal: Production, Perception, and Acoustic-Phonetic Characterization. 2.2. The Speech-Production Process. 2.3. Representing Speech in the Time and Frequency Domains. 2.4. Speech Sounds and Features. 2.5. Approaches to Automatic Speech Recognition by Machine -- 3. Signal Processing and Analysis Methods for Speech Recognition. 3.2. The Bank-of-Filters Front-End Processor. 3.3 Linear Predictive Coding Model for Speech Recognition. 3.4. VectorQuantization. 3.5. Auditory-Based Spectral Analysis Models -- 4.Pattern-Comparison Techniques. 4.2. Speech (Endpoint) Detection. 4.3.Distortion Measures - Mathematical Considerations. 4.4. Distortion Measures - Perceptual Considerations. 4.5. Spectral-Distortion Measures. 4.6. Incorporation of Spectral Dynamic Features into the Distortion Measure 4.7. Time Alignment and Normalization.
|
| 505 |
2 |
0 |
|a 1. Fundamentals of Speech Recognition. 1.2. The Paradigm for SpeechRecognition. 1.3. Outline. 1.4. A Brief History of Speech-RecognitionResearch -- 2. The Speech Signal: Production, Perception, andAcoustic-Phonetic Characterization. 2.2. The Speech-Production Process.2.3. Representing Speech in the Time and Frequency Domains. 2.4. SpeechSounds and Features. 2.5. Approaches to Automatic Speech Recognition byMachine -- 3. Signal Processing and Analysis Methods for SpeechRecognition. 3.2. The Bank-of-Filters Front-End Processor. 3.3. LinearPredictive Coding Model for Speech Recognition. 3.4. VectorQuantization. 3.5. Auditory-Based Spectral Analysis Models -- 4.Pattern-Comparison Techniques. 4.2. Speech (Endpoint) Detection. 4.3.Distortion Measures - Mathematical Considerations. 4.4. DistortionMeasures - Perceptual Considerations. 4.5. Spectral-DistortionMeasures. 4.6. Incorporation of Spectral Dynamic Features into theDistortion Measure. 4.7. Time Alignment and Normalization.
|
| 650 |
1 |
0 |
|a Automatic speech recognition
|
| 650 |
1 |
0 |
|a Speech processing systems
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| 650 |
1 |
0 |
|a Συστήματα επεξεργασίας ομιλίας
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| 650 |
1 |
0 |
|a Automatic speech recognition
|
| 650 |
1 |
0 |
|a Speech processing systems
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| 700 |
1 |
0 |
|a Juang, B. H.
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| 700 |
1 |
0 |
|a Juang, B. H.
|
| 700 |
1 |
0 |
|a Juang, B. H.
|
| 710 |
1 |
0 |
|a Prentice-Hall PTR
|
| 952 |
|
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|a GR-AtPPV
|b 59ccd0546c5ad13446099a17
|c 998a
|d 945l
|e 006.454 RAB
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| 952 |
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|a GR-AtNTU
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