1. A method comprising: determining 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; identifying one or more finite-state machines (“FSMs”) that correspond to one of the one or more phonemes included in the one or more linguistic targets, wherein each of the one or more FSMs includes 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 a positive integer; determining one or more possible sequences of synthetic speech models based on the phonemic representation of the text, wherein each of the one or more possible sequences includes at least one FSM; determining, 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 the text; and generating, by a computing system having a processor and a memory, a synthetic speech signal of the text 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.