| 1. | On the importance of components of the mfcc in speech and speaker recognition
语音识别和说话人识别中各倒谱分量的相对重要性 |
| 2. | In this paper , we present a way of audio recognition based on mfcc and analyze mel coefficient deeply
本文提出一种基于均值mfcc的音频信号识别算法,对mfcc系数进运算行了深入的分析。 |
| 3. | The theory of lpc and mel - scaled cepstrum analysis is introduced in this dissertation and how to extract lpcc and mfcc is elaborated
详细介绍了线性预测分析( lpc )和mel倒谱分析的原理及其具体实现过程。 |
| 4. | The major work and achievement of this paper are presented as follows : ( 1 ) we review the main methods of audio retrieval at home and abroad
对音频信号的短时过零率、短时能量、 mel倒谱系数等进行主要分析。提出均值mfcc系数作为音频特征的方法。 |
| 5. | The paper discussed the bandpass filters analysis method and the technology of linear prediction code ， then reduced the lpcc and the mfcc parameters
本文还介绍了语音信号分析方法中的滤波器组分析方法和线性预测编码技术，并推导了lpcc参数和mfcc参数。 |
| 6. | Also , since mfcc represent hearing frequency nonlinear characteristic , we utilize mfcc to be another speak recognition characteristic parameter to distinguish the input passwords
利用听觉频率非线性特性的美尔倒谱作为语音识别的特征参数，来辨识说话人提供的输入口令。 |
| 7. | Through experiments , it proved that using average mel coefficients as characters , dtw arithmetic is an efficient arithmetic for recognizing single sound signal
均值mfcc系数作为音频特征,采用动态时间规整识别算法,经过大量实验证明,这种方法能有效地对单一音频信号进行识别。 |
| 8. | ( 3 ) study the segmentation and recognition of audio frequency signal . audio signal can be divided into segments based on zero - crossing rate . ( 4 ) a audio recognition arithmetic based on mfcc is proposed
音频信号的处理作为项目的一部分,根据要求实现了对单一音频信号的识别,用vc6 . 0来实现。 |
| 9. | ( 2 ) study the character of audio signal . analyze the zero - crossing rate and mfcc . a mean mel coefficients is proposed , it can be used to recognized different audio signal
通过对mfcc系数进行分析,均值mfcc系数作为音频特征,采用动态时间规整识别算法,能够对单一音频进行识别,对已有数据源进行测试,有较高的识别率。 |
| 10. | In this thesis , first we analyzed and designed a traditional continued speech recognition system , which based on hmm and mfcc speech features . then we researched some noise robust technologies based on that system
本论文首先分析并实现了一个以mel频率倒谱系数( mfcc )作为语音特征,基于隐马尔可夫模型( hmm ) ,针对连续数字串识别任务的基本连续语音识别系统。 |
