Automatic speech recognition (ASR) by machine has received a great deal of attention in past decades. Speech recognition algorithms based on the Mel frequency cepstrum coefficient (MFCC) and the hidden Markov model (HMM) have a better recognition performance compared with other speech recognition algorithms and are widely used in many applications. In this thesis a speech recognition system with an efficient MFCC extraction algorithm and multi-mixture models is presented. It is composed of two parts: a MFCC feature extractor and a HMM-based speech decoder. / For the HMM-based decoder of the speech recognition system, it is advantageous to use models with multi mixtures, but with more mixtures the calculation becomes more complicated. Using a table look-up method proposed in this thesis the new design can handle up to 16 states and 8 mixtures. This new design can be easily extended to handle models which have more states and mixtures. We have implemented the new algorithm with an Altera FPGA chip using fix-point calculation and tested the FPGA chip with the speech data from the AURORA 2 database, which is a well known database designed to evaluate the performance of speech recognition algorithms in noisy conditions [27]. The recognition accuracy of the new system is 91.01%. A conventional software recognition system running on PC using 32-bit floating point calculation has a recognition accuracy of 94.65%. / In the conventional MFCC feature extraction algorithm, speech is separated into some short overlapped frames. The existing extraction algorithm requires a lot of computations and is not suitable for hardware implementation. We have developed a hardware efficient MFCC feature extraction algorithm in our work. The new algorithm reduces the computational power by 54% compared to the conventional algorithm with only 1.7% reduction in recognition accuracy. / Han Wei. / "September 2006." / Adviser: Cheong Fat Chan. / Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1823. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 108-111). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_343925 |
| Date | January 2006 |
| Contributors | Han, Wei., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (x, 255 p. : ill.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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