• Produktbild: Automatic Speech Recognition
  • Produktbild: Automatic Speech Recognition
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Automatic Speech Recognition A Deep Learning Approach

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138,99 € UVP 160,49 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.09.2016

Abbildungen

XXVI, 321 p. 62 illus.

Verlag

Springer London

Seitenzahl

321

Maße (L/B/H)

23,5/15,5/1,9 cm

Gewicht

528 g

Auflage

Softcover reprint of the original 1st ed. 2015

Sprache

Englisch

ISBN

978-1-4471-6967-3

Beschreibung

Rezension

“Deep Learning (DL) has demonstrated a phenomenal success in various AI applications. … This book by two leading experts in Deep Learning is certainly a welcome addition to the literature of the field, particularly in automatic speech recognition. … this book presents a very valuable vista of the state-of-art of Deep Learning, focusing on speech recognition applications.” (Robert Kozma, Mathematical Reviews, September, 2017)






“The book addresses real-world problems of current interest regarding automatic speech recognition. … This book is useful for all researchers working in automatic speech recognition as well as in real-world applications of deep learning.” (Ruxandra Stoean, zbMATH 1356.68004, 2017)

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.09.2016

Abbildungen

XXVI, 321 p. 62 illus.

Verlag

Springer London

Seitenzahl

321

Maße (L/B/H)

23,5/15,5/1,9 cm

Gewicht

528 g

Auflage

Softcover reprint of the original 1st ed. 2015

Sprache

Englisch

ISBN

978-1-4471-6967-3

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Automatic Speech Recognition
  • Produktbild: Automatic Speech Recognition
  • Section 1: Automatic speech recognition: Background.- Feature extraction: basic frontend.- Acoustic model: Gaussian mixture hidden Markov model.- Language model: stochastic N-gram.- Historical reviews of speech recognition research: 1st, 2nd, 3rd, 3.5th, and 4th generations.- Section 2: Advanced feature extraction and transformation.- Unsupervised feature extraction.- Discriminative feature transformation.- Section 3: Advanced acoustic modeling.- Conditional random field (CRF) and hidden conditional random field (HCRF).- Deep-Structured CRF.- Semi-Markov conditional random field.- Deep stacking models.- Deep neural network – hidden Markov hybrid model.- Section 4: Advanced language modeling.- Discriminative Language model.- Log-linear language model.- Neural network language model.