A computer-implemented method for recognizing and understanding spoken commands that includes one or more proper name entities, comprising: receiving an utterance (825a, 825b, 1110) from a user; performing primary automatic speech recognition processing upon said utterance with a primary automatic speech recognizer to output a dataset comprising at least a sequence of nominal transcribed words and start and end times for each nominal transcribed word within said utterance; performing natural language understanding processing upon said dataset with a natural language understanding processor to generate and augment the dataset with a nominal meaning including a semantic meaning for the utterance and to determine the putative presence and type of one or more spoken proper name entities within said utterance, each with a corresponding acoustic span, wherein each acoustic span comprises a contiguous section of audio within said utterance determined from said start and end times of the words of the corresponding putative proper name entity as transcribed by the primary automatic speech recognizer, providing transcriptions meaning hypotheses, each hypothesis having a natural language understanding, NLU, confidence score; performing secondary automatic speech recognition processing upon each said acoustic span with a secondary automatic speech recognizer, in each instance said secondary automatic speech recognizer specialized to process a given putative type of acoustic span to generate one or more transcriptions and associated semantic meanings for each said acoustic span; substituting the one or more transcriptions and associated semantic meanings obtained from each secondary recognition as appropriate within the dataset to revise the results of the primary automatic speech recognizer and natural language understanding processor to generate a plurality of alternative transcriptions with one or more associated meaning hypotheses, each hypothesis having an automatic speech recognition, ASR, confidence score; determining a final confidence score based on at least one of said ASR confidence score and NLU confidence score of each alternative transcription; and outputting one or more alternative transcriptions and associated semantic meanings for the entire utterance based on said final confidence scores. receiving an utterance (825a, 825b, 1110) from a user; performing primary automatic speech recognition processing upon said utterance with a primary automatic speech recognizer to output a dataset comprising at least a sequence of nominal transcribed words and start and end times for each nominal transcribed word within said utterance; performing natural language understanding processing upon said dataset with a natural language understanding processor to generate and augment the dataset with a nominal meaning including a semantic meaning for the utterance and to determine the putative presence and type of one or more spoken proper name entities within said utterance, each with a corresponding acoustic span, wherein each acoustic span comprises a contiguous section of audio within said utterance determined from said start and end times of the words of the corresponding putative proper name entity as transcribed by the primary automatic speech recognizer, providing transcriptions meaning hypotheses, each hypothesis having a natural language understanding, NLU, confidence score; performing secondary automatic speech recognition processing upon each said acoustic span with a secondary automatic speech recognizer, in each instance said secondary automatic speech recognizer specialized to process a given putative type of acoustic span to generate one or more transcriptions and associated semantic meanings for each said acoustic span; substituting the one or more transcriptions and associated semantic meanings obtained from each secondary recognition as appropriate within the dataset to revise the results of the primary automatic speech recognizer and natural language understanding processor to generate a plurality of alternative transcriptions with one or more associated meaning hypotheses, each hypothesis having an automatic speech recognition, ASR, confidence score; determining a final confidence score based on at least one of said ASR confidence score and NLU confidence score of each alternative transcription; and outputting one or more alternative transcriptions and associated semantic meanings for the entire utterance based on said final confidence scores.