A Speech Recognition System for Embedded Applications Using the SOM and TS-SOM Networks

摘要

The self-organizing map (SOM) (Kohonen, 1982) is one of the most important neural network architecture. Since its invention it has been applied to so many areas of Science and Engineering that it is virtually impossible to list all the applications available to date (van Hulle, 2010; Yin, 2008). In most of these applications, such as image compression (Amerijckx et al., 1998), time series prediction (Guillen et al., 2010; Lendasse et al., 2002), control systems (Cho et al., 2006; Barreto & Araujo, 2004), novelty detection (Frota et al., 2007), speech recognition and modeling (Gas et al., 2005), robotics (Barreto et al., 2003) and bioinformatics (Martin et al., 2008), the SOM is designed to be used by systems whose computational resources (e.g. memory space and CPU speed) are fully available. However, in applications where such resources are limited (e.g. embedded software systems, such as mobile phones), the SOM is rarely used, especially due to the cost of the best-matching unit (BMU) search (Sagheer et al., 2006). Essentially, the process of developing automatic speech recognition (ASR) systems is a challenging tasks due to many factors, such as variability of speaker accents, level of background noise, and large quantity of phonemes or words to deal with, voice coding and parameterization, among others. Concerning the development of ASR applications to mobile phones, to all the aforementioned problems, others are added, such as battery consumption requirements and low microphone quality. Despite those difficulties, with the significant growth of the information processing capacity of mobile phones, they are being used to perform tasks previously carried out only on personal computers. However, the standard user interface still limits their usability, since conventional keyboards are becoming smaller and smaller. A natural way to handle this new demand of embedded applications is through speech/voice commands. Since the neural phonetic typewriter (Kohonen, 1988), the SOM has been used in a standalone fashion for speech coding and recognition (see Kohonen, 2001, pp. 360-362). Hybrid architectures, such as SOM with MultiLayer Perceptrons (SOM-MLP) and SOM with Hidden Markov Models (SOM-HMM), have also been proposed (Gas et al., 2005; Somervuo, 2000). More specifically, studies involving speech recognition in mobile devices systems include those by Olsen et al. (2008); Alhonen et al. (2007) and Varga & Kiss (2008). It is worth noticing that Portuguese is the eighth, perhaps, the seventh most spoken language worldwide and the third among the Western countries, after English and Spanish. Despite 6