Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.contributor.advisor | Chi, Zheru (EIE) | - |
| dc.creator | Gao Wuhang | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/8143 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | User-dependent voice dialling on smartphones | en_US |
| dcterms.abstract | Speech recognition is a hot topic in machine intelligence. One important application of speech recognition is for human computer interaction by "talking to machines". It has been a technology frequently portrayed in science fiction films. However, due to the complexity of the problem, speech recognition technology is still not very mature in practice. Cloud based speech recognition is a promising way to handle this task. In this dissertation, we report the development of a voice dialling system for smart phones. The user of a smartphone can dial a number by reading out the name of the person to be phoned. In this dissertation, speech recognition techniques are first reviewed. Most existing speech recognition algorithms have been found not suitable for mobile devices due to very high computational complexity. In our investigation, we use Core Audio Format (CAF) format to record voices, use Mel-Frequency Cepstral Coefficient (MFCC) to extract the eigenvectors of speech signal. Based on Dynamic Time Warping (DTW), we propose a DTW based linear comparison algorithm to perform user-dependent voice dialling on smartphones. The user of a smartphone first stores speech samples. The signal processing and feature extraction are carried out after the data are collected. The application works locally without a server so the network speed and safety are not issues. Also this application is language independent. The voice matching rate is highly related to the quality of samples. The performance of this application is evaluated on IOS simulator and IPhone4s. The application can achieve a correct matching rate of 88%. | en_US |
| dcterms.extent | 40 leaves : illustrations ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2014 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Mobile computing. | en_US |
| dcterms.LCSH | Automatic speech recognition. | en_US |
| dcterms.LCSH | Speech processing systems. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b28194810.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.55 MB | Adobe PDF | View/Open |
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