Speech recognition on DSP: algorithm optimization and performance analysis.

Yuan Meng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 85-91).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- History of ASR development --- p.2Chapter 1.2 --- Fundamentals of automatic speech recognition --- p.3Chapter 1.2.1 --- Classification of ASR systems --- p.3Chapter 1.2.2 --- Automatic speech recognition process --- p.4Chapter 1.3 --- Performance measurements of ASR --- p.7Chapter 1.3.1 --- Recognition accuracy --- p.7Chapter 1.3.2 --- Complexity --- p.7Chapter 1.3.3 --- Robustness --- p.8Chapter 1.4 --- Motivation and goal of this work --- p.8Chapter 1.5 --- Thesis outline --- p.10Chapter 2 --- Signal processing techniques for front-end --- p.12Chapter 2.1 --- Basic feature extraction principles --- p.13Chapter 2.1.1 --- Pre-emphasis --- p.13Chapter 2.1.2 --- Frame blocking and windowing --- p.13Chapter 2.1.3 --- Discrete Fourier Transform (DFT) computation --- p.15Chapter 2.1.4 --- Spectral magnitudes --- p.15Chapter 2.1.5 --- Mel-frequency filterbank --- p.16Chapter 2.1.6 --- Logarithm of filter energies --- p.18Chapter 2.1.7 --- Discrete Cosine Transformation (DCT) --- p.18Chapter 2.1.8 --- Cepstral Weighting --- p.19Chapter 2.1.9 --- Dynamic featuring --- p.19Chapter 2.2 --- Practical issues --- p.20Chapter 2.2.1 --- Review of practical problems and solutions in ASR appli- cations --- p.20Chapter 2.2.2 --- Model of environment --- p.23Chapter 2.2.3 --- End-point detection (EPD) --- p.23Chapter 2.2.4 --- Spectral subtraction (SS) --- p.25Chapter 3 --- HMM-based Acoustic Modeling --- p.26Chapter 3.1 --- HMMs for ASR --- p.26Chapter 3.2 --- Output probabilities --- p.27Chapter 3.3 --- Viterbi search engine --- p.29Chapter 3.4 --- Isolated word recognition (IWR) & Connected word recognition (CWR) --- p.30Chapter 3.4.1 --- Isolated word recognition --- p.30Chapter 3.4.2 --- Connected word recognition (CWR) --- p.31Chapter 4 --- DSP for embedded applications --- p.32Chapter 4.1 --- "Classification of embedded systems (DSP, ASIC, FPGA, etc.)" --- p.32Chapter 4.2 --- Description of hardware platform --- p.34Chapter 4.3 --- I/O operation for real-time processing --- p.36Chapter 4.4 --- Fixed point algorithm on DSP --- p.40Chapter 5 --- ASR algorithm optimization --- p.42Chapter 5.1 --- Methodology --- p.42Chapter 5.2 --- Floating-point to fixed-point conversion --- p.43Chapter 5.3 --- Computational complexity consideration --- p.45Chapter 5.3.1 --- Feature extraction techniques --- p.45Chapter 5.3.2 --- Viterbi search module --- p.50Chapter 5.4 --- Memory requirements consideration --- p.51Chapter 6 --- Experimental results and performance analysis --- p.53Chapter 6.1 --- Cantonese isolated word recognition (IWR) --- p.54Chapter 6.1.1 --- Execution time --- p.54Chapter 6.1.2 --- Memory requirements --- p.57Chapter 6.1.3 --- Recognition performance --- p.57Chapter 6.2 --- Connected word recognition (CWR) --- p.61Chapter 6.2.1 --- Execution time consideration --- p.62Chapter 6.2.2 --- Recognition performance --- p.62Chapter 6.3 --- Summary & discussion --- p.66Chapter 7 --- Implementation of practical techniques --- p.67Chapter 7.1 --- End-point detection (EPD) --- p.67Chapter 7.2 --- Spectral subtraction (SS) --- p.71Chapter 7.3 --- Experimental results --- p.72Chapter 7.3.1 --- Isolated word recognition (IWR) --- p.72Chapter 7.3.2 --- Connected word recognition (CWR) --- p.75Chapter 7.4 --- Results --- p.77Chapter 8 --- Conclusions and future work --- p.78Chapter 8.1 --- Summary and Conclusions --- p.78Chapter 8.2 --- Suggestions for future research --- p.80Appendices --- p.82Chapter A --- "Interpolation of data entries without floating point, divides or conditional branches" --- p.82Chapter B --- Vocabulary for Cantonese isolated word recognition task --- p.84Bibliography --- p.8