Implementation of ASSR system based on HMM and syllable models on FPGA

Hidden Markov Models (HMMs) is one of the sophisticated methods for ASR system. In this work, we propose an Automatic Speech-Speaker Recognition (ASSR) system based on syllable models with HMM for speech expert on a FPGA platform. HMM is used to train the acoustic model for each phoneme evaluated on two databases, i.e. THCHS-30 (Tsinghua Chinese 30 hour database) and CMU ARCTIC Databases. The binary halved clustering (BHC) method which used binary-halved splitting to generate speaker models for low complexity requirement on speaker expert. The experiments are conducted on two types of platforms, including PC and Xilinx Spartan-6 FPGA. The experimental results indicate that the proposed work can achieve 90.8% accuracy rate at Mandarin speech recognition and 86.6% accuracy rate on English speech recognition, respectively. In addition, the work also evaluates OOV (Out of Vocabulary) which reaches 88.7% of detection rate on Mandarin and 84.9% of OOV detection rate on English as well. The speaker recognition rate also reaches to 81.3% and OOS (Out of Speaker) detection rate is 80.8%, respectively.