6. A method comprising: extracting features of input speech signals, by a feature extractor implemented by at least one hardware processor, and applying sequences of one or more labels to the extracted features; implementing, by the at least one hardware processor, a non-optimized composed weighted speech transducer based on signals representing a hidden Markov model (HMM) transducer, a context dependent phone model, a lexicon model for pronunciation dictionary, and an N-gram language model for sentence probability; and outputting decisions, by a decoder implemented by the at least one hardware processor, about the input speech signals based, at least in part, on the sequences of labels and the non-optimized composed weighted speech transducer.
6. A method comprising: extracting features of input speech signals, by a feature extractor implemented by at least one hardware processor, and applying sequences of one or more labels to the extracted features; implementing, by the at least one hardware processor, a non-optimized composed weighted speech transducer based on signals representing a hidden Markov model (HMM) transducer, a context dependent phone model, a lexicon model for pronunciation dictionary, and an N-gram language model for sentence probability; and outputting decisions, by a decoder implemented by the at least one hardware processor, about the input speech signals based, at least in part, on the sequences of labels and the non-optimized composed weighted speech transducer. 7. The method of claim 6 , further comprising: generating search graphs by the non-optimized composed weighted speech transducer.