论文标题
在嘈杂环境中不同编码率的隔离数字识别的最小功能分析
Minimal Feature Analysis for Isolated Digit Recognition for varying encoding rates in noisy environments
论文作者
论文摘要
这项研究工作是关于语音识别的最新发展。在这项研究工作中，在存在不同的比特速率和不同噪声水平的情况下对孤立的数字识别的分析。这项研究工作是使用Audacity和HTK工具包进行的。隐藏的马尔可夫模型（HMM）是用于执行此实验的识别模型。所使用的特征提取技术是MEL频率CEPSTRUM系数（MFCC），线性预测编码（LPC），感知线性预测（PLP），MEL SPECTRUM（MELSPEC），FILLE BANK（FBANK）。已经考虑了三种不同的噪声水平来测试数据。这些包括随机噪声，风扇噪声和实时环境中的随机噪声。这样做是为了分析可用于实时应用程序的最佳环境。此外，考虑了五种不同类型的以不同采样率的常用比特率，以找出最佳的比特率。
This research work is about recent development made in speech recognition. In this research work, analysis of isolated digit recognition in the presence of different bit rates and at different noise levels has been performed. This research work has been carried using audacity and HTK toolkit. Hidden Markov Model (HMM) is the recognition model which was used to perform this experiment. The feature extraction techniques used are Mel Frequency Cepstrum coefficient (MFCC), Linear Predictive Coding (LPC), perceptual linear predictive (PLP), mel spectrum (MELSPEC), filter bank (FBANK). There were three types of different noise levels which have been considered for testing of data. These include random noise, fan noise and random noise in real time environment. This was done to analyse the best environment which can used for real time applications. Further, five different types of commonly used bit rates at different sampling rates were considered to find out the most optimum bit rate.
