A method for synthesizing speech from surface electromyogram (SEMG) signals in a frame-by-frame basis is presented. The input SEMG signals of spoken words are blocked into frames from which SEMG features were extracted and classified into a number of phonetic classes by a neural network. A sequence of phonetic class labels is thus produced which was subsequently smoothed by applying an error correction technique. The speech waveform of a word is then constructed by concatenating the pre-recorded speech segments corresponding to the phonetic class labels. Experimental results show that the neural network can classify the SEMG features with 86.3% accuracy, this can be further improved to 96.4% by smoothing the phonetic class labels. Experimental evaluations based on the synthesis of eight words show that on average 92.9% of the words can be synthesized correctly. It is also demonstrated that the proposed frame-based feature extraction and conversion methodology can be applied to SEMG-based speech synthesis. / Although speech is the most natural means for communication among humans, there are situations in which speech is impossible or inappropriate. Examples include people with vocal cord damage, underwater communications or in noisy environments. To address some of the limitations of speech communication, non-acoustic communication systems using surface electromyogram signals have been proposed. However, most of the proposed techniques focus on recognizing or classifying the SEMG signals into a limited set of words. This approach shares similarities with isolated word recognition systems in that periods of silence between words are mandatory and they have difficulties in recognizing untrained words and continuous speech. / Lam Yuet Ming. / "December 2006." / Adviser: Leong Heng Philip Wai. / Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5392. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 104-111). / 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_343810 |
| Date | January 2006 |
| Contributors | Lam, Yuet Ming., Chinese University of Hong Kong Graduate School. Division of Computer Science and 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 (xiii, 111 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/) |
Page generated in 0.0017 seconds