Automatic Speech Recognition

ASR has already been researched and applied as a HCI for over thirty years [6]. Normally, in order to apply speech recognition, an entire system needs to be developed from scratch. Fortunately, several famous open source speech recognition systems are available on the Internet, such as HTK developed by Cambridge University [38, 39] (note: HTK has license restriction), Julius maintained by Kyoto University [16], ISIP speech recognition environment by Mississippi State University [7, 23] and CMU Sphinx [18, 30]. Modern general-purpose speech recognition systems, including all the above four open source ASR systems, are generally based on Mel Frequency Cepstral Coefficients (MFCCs) for audio signal presentation and Hidden Morkov Models (HMMs) to model the speech stochastic process. HMM was first described in a series of statistical papers by Baum [3] and some other authors in the 1960’s. Speach recognition reseach based on HMM started in the mid-1970s. A representative ASR at that time is Dragon [1, 2]. After that, variants of HMM were explored, such as discrete HMMs [19], semicontinuous HMMs [13–15] and continuous HMMs [24]. In 1989, Rabiner reviewed HMM theories and summarized some problems in speach recognition that could be modeled by HMM [25]. All the above HMM related researches are summarized in the online tutorial “Ten years of HMMs” [5]. In fact, from the viewpoint of pattern recognition and machine learning, HMM, as well as another famous control model Kalman filter could be viewed as examples of Dynamic Bayesian Networks (DBNs) [10]. Murphy reviewed this point in his PhD thesis [21] and stated HMMs has limited “expressive power” but DBNs generalize HMMs by allowing the state space to be represented in factored form, rather than a single discreted random variable. Therefore, DBNs, also known as directed graphic models [4] should be a more generalized tool for speech recognition. In fact, early in 1995, Zweig has already put DBNs into speech recognition in his PhD thesis [40]. The entire architecture of CMU Sphinx4 speech recognition system is cited in figure 1. In the following section, how to generally construct an entire ASR system is elaborated. Figure 1: Sphinx4 ASR Framework [30] 0.1 Model the Speech Stochastic Process 0.1. 1 Discretize the Speech Signal Substantially, speech can be looked on as a continuous stochastic process. Operably, it is dealt with as a discrete stochastic process by sampling first and then windowing. But how to discretize the speech signal? We first denote the sample rate as Sr; • The frequency range of the sound that human being can hear is between 20 to 20,000Hz. According to Nyquist theory, in order to be able to analyze the sound (1D signal) which can be heard, a sample rate of at least 2*20,000 is necessary. The most common sample rate for speech/music is Sr = 44, 100Hz, which absolutely satisfies Nyquist theory. • Refer to [33], the frequency of ordinary human speech varies between 512Hz and 2,048Hz, between 2,048Hz and 8,192Hz for labial and fricative sounds, and from 8,192Hz to 11,000Hz for the sound of letter “S”. Therefore, according to Nyquist theory, the sample rate could be just over 11,000*2=22,000Hz. That’s why Sr = 22, 050Hz is also often used