Automatic Speech Recognition on Mobile Devices and over - download pdf or read online

By Zheng-Hua Tan BSc, MSc, PhD, Børge Lindberg MSc (auth.)

The notable advances in computing and networking have sparked a major curiosity in deploying automated speech reputation on cellular units and over verbal exchange networks. This development is accelerating.

This publication brings jointly prime educational researchers and business practitioners to handle the problems during this rising realm and offers the reader with a accomplished creation to the topic of speech reputation in units and networks. It covers community, allotted and embedded speech reputation platforms, that are anticipated to co-exist sooner or later. It deals a wide-ranging, unified method of the subject and its most modern improvement, additionally overlaying the main up to date criteria and several other off-the-shelf systems.

Key features:

• presents an in-depth evaluation of community speech popularity, disbursed speech reputation, embedded speech popularity, platforms and applications

• starts with a complete assessment of the topic, discussing the professionals and cons of the provided methods, and guiding the reader throughout the following chapters

• comprises structures like cell phones, PDAs and automobiles

• offers state of the art equipment, complicated structures, and the most recent standards

• deals operating wisdom wanted for either learn and practice

• References supplemental fabric at linked complementary web site at: http://asr.es.aau.dk

This all-inclusive text/reference is a vital learn for graduate scholars, scientists and engineers operating or learning within the box of speech reputation and processing. It bargains a self-contained method of this scorching learn topic.

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Extra resources for Automatic Speech Recognition on Mobile Devices and over Communication Networks

Example text

And Shao, X. (2007) Prediction of fundamental frequency and voicing from Melfrequency cepstral coefficients for unconstrained speech reconstruction. IEEE Transactions on Audio, Speech and Language Processing, vol. 15, no. 1, pp. 24–33. Novak, M. (2004) Towards large vocabulary ASR on embedded platforms. In Proceedings of ICSLP, Jeju Island, Korea. Pearce, D. (2000) Enabling new speech driven services for mobile devices: An overview of the ETSI standards activities for distributed speech recognition front-ends.

1 Experimental Setup Our continuous French speech recognition system uses the Janus-III toolkit from CMU (Finke et al. 1997). The context dependent acoustic model (750 CD codebooks, 16 Gaussians each) was learned on a corpus, which contains 12 h of continuous speech of 72 speakers extracted from Bref 80 database (Lamel et al. 1991). The system uses 24-dimensional LDA features obtained from 43-dimensional acoustic vectors (13 MFCC, 13 ǻMFCC, 13 ǻǻMFCC, E, ǻE, ǻǻE, zero-crossing parameter) and extracted every 10 ms.

1997). The context dependent acoustic model (750 CD codebooks, 16 Gaussians each) was learned on a corpus, which contains 12 h of continuous speech of 72 speakers extracted from Bref 80 database (Lamel et al. 1991). The system uses 24-dimensional LDA features obtained from 43-dimensional acoustic vectors (13 MFCC, 13 ǻMFCC, 13 ǻǻMFCC, E, ǻE, ǻǻE, zero-crossing parameter) and extracted every 10 ms. The vocabulary contains nearly 5,500 phonetic variants of 2,900 distinct words; it is specific to the tourist reservation and information domain.

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