18. A computing system comprising: a data storage having stored therein program instructions and a plurality of fixed state machines (“FSMs”), wherein each FSM in the plurality of FSMs is a compressed recorded speech unit that simulates a Hidden Markov Model (“HMM”) by averaging one or more spectral features of a recorded speech unit over N states, wherein N is positive integer; and a processor that, upon executing the program instructions stored in the data storage, is configured to cause the computing system to: determine a phonemic representation of text that includes one or more linguistic targets, wherein each of the one or more linguistic targets includes one or more phonemes; identify one or more FSMs included in the plurality of FSMs that correspond to one of the one or more phonemes included in the one or more linguistic targets; determine one or more possible sequences of synthetic speech models based on the phonemic representation of text, wherein each of the one or more possible sequences includes at least one FSM; determine, from the one or more possible sequences of synthetic speech models, a selected sequence that minimizes a value of a cost function, wherein the cost function represents a likelihood that one of the one or more possible sequences substantially matches the phonemic representation of text; and generate a synthetic speech signal based on the selected sequence, wherein the synthetic speech signal includes information indicative of one or more spectral features generated from at least one FSM included in the selected sequence.
18. A computing system comprising: a data storage having stored therein program instructions and a plurality of fixed state machines (“FSMs”), wherein each FSM in the plurality of FSMs is a compressed recorded speech unit that simulates a Hidden Markov Model (“HMM”) by averaging one or more spectral features of a recorded speech unit over N states, wherein N is positive integer; and a processor that, upon executing the program instructions stored in the data storage, is configured to cause the computing system to: determine a phonemic representation of text that includes one or more linguistic targets, wherein each of the one or more linguistic targets includes one or more phonemes; identify one or more FSMs included in the plurality of FSMs that correspond to one of the one or more phonemes included in the one or more linguistic targets; determine one or more possible sequences of synthetic speech models based on the phonemic representation of text, wherein each of the one or more possible sequences includes at least one FSM; determine, from the one or more possible sequences of synthetic speech models, a selected sequence that minimizes a value of a cost function, wherein the cost function represents a likelihood that one of the one or more possible sequences substantially matches the phonemic representation of text; and generate a synthetic speech signal based on the selected sequence, wherein the synthetic speech signal includes information indicative of one or more spectral features generated from at least one FSM included in the selected sequence. 19. The computing system of claim 18 , wherein each of the N states of each FSM is based on a mean and a variance of one or more vectors aligned in a given state, wherein the one or more vectors are indicative of one or more spectral features of a segment of an associated recorded speech unit, and wherein N means and N variances are used to estimate a multi-mixture Gaussian density function in order to simulate an HMM.