篇名 |
Modelling of a Speech-to-Text Recognition System for Air Traffic Control and NATO Air Command
|
---|---|
並列篇名 | Modelling of a Speech-to-Text Recognition System for Air Traffic Control and NATO Air Command |
作者 | Grant Zietsman、Reza Malekian |
英文摘要 | Accent invariance in speech recognition is a chal- lenging problem especially in the are of aviation. In this paper a speech recognition system is developed to transcribe accented speech between pilots and air traffic controllers. The system allows handling of accents in continuous speech by modelling phonemes using Hidden Markov Models (HMMs) with Gaussian mixture model (GMM) probability density functions for each state. These phonemes are used to build word models of the NATO phonetic alphabet as well as the numerals 0 to 9 with transcriptions obtained from the Carnegie Mellon University (CMU) pronouncing dictionary. Mel-Frequency Cepstral Co-efficients (MFCC) with delta and delta-delta coefficients are used for the feature extraction process. Amplitude normalisation and covariance scaling is implemented to improve recognition accuracy. A word error rate (WER) of 2% for seen speakers and 22% for unseen speakers is obtained.
|
起訖頁 | 1527-1539 |
關鍵詞 | Automatic Speech Recognition (ASR)、Hidden Markov Model (HMM)、Gaussian Mixture Model (GMM)、Mel-Frequency Cepstral Coefficients (MFCC)、Covariance scaling |
刊名 | 網際網路技術學刊 |
期數 | 202212 (23:7期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
|
QR Code | |
該期刊 上一篇
| Developing a Multifunctional Heating Pad Based on Fuzzy-Edge Computations and IoMT Approach |
該期刊 下一篇
| Rafflesia Optimization Algorithm Applied in the Logistics Distribution Centers Location Problem |