This paper introduce an approach to study the effects of different levels of
environment noise on the recognition rate of speech recognition systems, which are
not used any type of filters to deal with this issue. This is achieved by implementing
an embedded SoPC (System on a Programmable Chip) technique with Altera Nios II
processor for real-time speech recognition system. Mel Frequency Cepstral
Coefficients (MFCCs) technique was used for speech signal feature extraction
(observation vector). Model the observation vector of voice information by using
Gaussian Mixture Model (GMM), this model passed to the Hidden Markov Model
(HMM) as probabilistic model to process the GMM statistically to make decision on
utterance words recognition, whether a single or composite, one or more syllable
words. The framework was implemented on Altera Cyclone II EP2C70F896C6N
FPGA chip sitting on ALTERA DE2-70 Development Board. Each word model
(template) stored as Transition Matrix, Diagonal Covariance Matrices, and Mean
Vectors in the system memory. Each word model utilizes only 4.45Kbytes regardless
of the spoken word length. Recognition words rate (digit/0 to digit/10) given 100%
for the individual speaker. The test was conducted at different sound levels of the
surrounding environment (53dB to 73dB) as measured by Sound Level Meter (SLM)