*3.6.2. Speech recognition*

Speech recognition process starts by acquiring the sound from a user using microphone, and then, the series of acoustic signals are converted to a set of identifying words. The speech recognition depends on many factors such as language model, vocabulary size, speaking style, speaker enrollment, and transducer [15]. Speech recognition system is classified to "speaker dependent system," if the user should train the system before using it, and to "speaker independent system," if the system can recognize any speaker's speech without the need to train phase. Speech recognition systems can also be divided into "isolated word speech" or "continuous speech" based on the number of the used vocabularies for identification process.

Speaker models [16, 17] enable us to generate the scores from which we will make decisions. As in any pattern recognition problem, the choices are numerous, and the most popular and dominated technique in last two decade is Hidden Markov Models. There are also other techniques used for speech recognition systems such as Artificial Neural Networks (ANN), Back Propagation Algorithm (BPA), Fast Fourier Transform (FFT), Learn Vector Quantization (LVQ), and Neural Networks (NN). A typical speech recognition system is shown in **Figure 6**.
