Speech Recognition: A

Speech recognition can be formulated as the problem of guessing a sequence of words that produces a sequence of sounds. The human brain is remarkably good at solving this problem, even though the same words correspond to many different sounds, because of accents or characteristics of the voice. Moreover, the environment is always noisy, to that the listeners hear a corrupted version of the speech.Computers are getting much better at speech recognition and voice command systems are now common for smartphones (Siri), automobiles (GPS, music, and climate control), call centers, and dictation systems. In this chapter, we explain the main ideas behind the algorithms for speech recognition and for related applications.The starting point is a model of the random sequence (e.g., words) to be recognized and of how this sequence is related to the observation (e.g., voice). The main model is called a hidden Markov chain. The idea is that the successive parts of speech form a Markov chain and that each word maps randomly to some sounds. The same model is used to decode strings of symbols in communication systems.Section 11.1 is a general discussion of learning. The hidden Markov chain model used in speech recognition and in error decoding is introduced in Sect. 11.2. That section explains the Viterbi algorithm. Section 11.3 discusses expectation maximization and clustering algorithms. Section 11.4 covers learning for hidden Markov chains.

Asynchronous Fault Detection H2 Filter For Markov Jump Linear Systems

This work focuses on the Fault Detection (FD) problem in the Markovian Jump Linear System framework for the discrete-time domain, under the assumption that the Markov chain mode is not directly accessible. This assumption poses new challenges, since the filter responsible for the residue generation no longer depends on the Markov chain mode. For modeling this type of situation, a Hidden Markov chain (O(k), Ô(k)) is considered, with O(k) corresponding to the hidden part and Ô(k), to the observable part. The main result is the design of an H2 Fault Detection Filter (FDF) that depends only on the estimated mode Ô(k), obtained through a formulation based on Linear Matrix Inequalities (LMIs). In order to illustrate the usability of the proposed approach, we consider as an illustrative example a plant with coupled tanks subject to two distinct faults.