Influence of Noisy Environment on the Speech Recognition Rate Based on the Altera FPGA

Abstract

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) instrument.

Keywords

• mel frequency cepstral coefficients
• gaussian mixture model
• hidden markov model
• system on a programmable chip
• nios ii processor