Abstract: A system and method for segmenting an audio file. The method includes analyzing an audio file, wherein the analyzing includes identifying speech recognition features within the audio file; generating metadata based on the audio file, wherein the metadata includes transcription characteristics of the audio file; and determining a segmenting interval for the audio file based on the speech recognition features and the metadata.

Abstract: A system and method for assessing transcription costs based on an audio file are provided. The method includes method for assessing an audio file for transcription includes accessing at least one audio file for transcription assessment; analyzing the at least one audio file to determine at least one transcription characteristic based on the at least one audio file; and calculating, based on the at least one determined transcription characteristic, an initial bid value for transcription of the audio file.

Abstract: Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents.

Abstract: A system and method for validating and correcting transcriptions of an audio file. The method includes analyzing an audio file to at least identify transcription characteristics of the audio file; comparing a received transcription file to the identified transcription characteristics; and validating the received transcription file to detect errors within the received transcription file.

Abstract: A system and method for segmenting an audio file. The method includes analyzing an audio file, wherein the analyzing includes identifying speech recognition features within the audio file; generating metadata based on the audio file, wherein the metadata includes transcription characteristics of the audio file; and determining a segmenting interval for the audio file based on the speech recognition features and the metadata.

Abstract: A system and method for assessing transcription costs based on an audio file are provided. The method includes method for assessing an audio file for transcription includes accessing at least one audio file for transcription assessment; analyzing the at least one audio file to determine at least one transcription characteristic based on the at least one audio file; and calculating, based on the at least one determined transcription characteristic, an initial bid value for transcription of the audio file.

Abstract: Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents.

Abstract: Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents.

Abstract: Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents.

Abstract: Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents.

Abstract: Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents.