Speaker-Independent Isolated Word Recognition
Authors: Yaxin Zhang
Venue:  (1996)

Speech recognition is a natural ability of human beings, but is a difficult job for current personal computers and workstations. Although dramatic achievements have been obtained during the past 40 years of automatic speech recognition (ASR) history, and many speech recognition products ranging from small vocabulary isolated word recognizers to large vocabulary continuous speech recognition systems have been found in the market, that the computing machine could arbitrarily recognize speech without any constraints in speakers, environments, and vocabulary still is a hopeful dream. It is this dream that has been involving quite a few generations of phoneticians, linguistics, and engineers in this area. The most popular strategy for ASR is the hidden Markov modeling (HMM) which has been used in most state-of-the-art ASR systems in the world. Hidden Markov modeling is a technique for the study of observed items arranged in a discrete-time series. The items in the series can be discretely or continuously distributed. The spectral feature vectors of speech signal are such series. The HMM has been shown to represent one of the most powerful statistical tools available for modeling speech signal. There are three basic types of HMM system, the discrete HMM system (DHMM), the continuous HMM system (CHMM), and the semi-continuous HMM system (SCHMM). In this thesis we mainly studied the DHMM system for speaker-independent isolated word recognition. Like any pattern recognition problem, the basic strategy for automatic speech recognition is the signal space partition which classifies the speech space into a number of regions and generates in each region a representative which may be a typical vector or a $pdf$ estimation of the region. Such representatives can be called the basic recognition templates. For example, the speech space partitioning in a DHMM system is performed by the vector quantizer and the VQ codebook is a collection of the basic templates. In a CHMM system, however, the Gaussian $pdf$ in a mixture generated for each state of each recognition model is the basic template. This thesis focuses on the speech space partition stage in a HMM recognizer to improve both the partitioning efficiency and the recognition accuracy. Three algorithms, the LBG algorithm, the EM algorithm, and the MGC algorithm, were investigated for the vector quantization in a DHMM isolated word recognizer. The investigation revealed an interesting phenomenon that the three VQ methods partitioned the speech space into different regions based on their own classification view, and eventually resulted in different wrong recognitions. Based on the complementory nature of the three VQ strategies, a multi-VQ multi-HMM recognizer was proposed and implemented. The experiments showed that this system generated a 50\% error rate reduction over the best single model system. This recognizer may be implemented in a parallel hardware structure with a little storage increase and The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player