Abstract: Some embodiments include a transcription knowledge graph that can resolve automatic speech recognition (ASR) engine output errors. In some embodiments, a transcription knowledge graph can utilize data from past sessions of the ASR engine to form a voice graph that can be analyzed to determine a correlation between a mis-transcription (error text) and the correct transcription (correct text). Thus, ASR engine outputs, even if they include a mis-transcription, can be adjusted to the correct transcription. Further, the correct transcriptions and the voice graph can be used to train machine learning (ML) algorithms to generate numerical representations of an entity. The ML algorithms can be applied to a transcription to correctly identify a corresponding entity label, even if the transcription was not utilized in the voice graph to train the ML algorithm.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and computer program product embodiments for adapting an automated speech recognition system to provide more accurate suggestions to voice queries involving media content including recently created or recently available content. An example computer-implemented method includes transcribing the voice query, identifying respective components of the query such as the media content being requested and the action to be performed, and generating fuzzy candidates that potentially match the media content based on phonetic representations of the identified components. Phonetic representations of domain specific candidates are stored in a domain entities index and is continuously updated with new entries so as to maintain the accuracy of the speech recognition of voice queries for recently created or recently available content.

Abstract: Some embodiments include a transcription knowledge graph that can resolve automatic speech recognition (ASR) engine output errors. In some embodiments, a transcription knowledge graph can utilize data from past sessions of the ASR engine to form a voice graph that can be analyzed to determine a correlation between a mis-transcription (error text) and the correct transcription (correct text). Thus, ASR engine outputs, even if they include a mis-transcription, can be adjusted to the correct transcription. Further, the correct transcriptions and the voice graph can be used to train machine learning (ML) algorithms to generate numerical representations of an entity. The ML algorithms can be applied to a transcription to correctly identify a corresponding entity label, even if the transcription was not utilized in the voice graph to train the ML algorithm.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and computer program product embodiments for adapting an automated speech recognition system to provide more accurate suggestions to voice queries involving media content including recently created or recently available content. An example computer-implemented method includes transcribing the voice query, identifying respective components of the query such as the media content being requested and the action to be performed, and generating fuzzy candidates that potentially match the media content based on phonetic representations of the identified components. Phonetic representations of domain specific candidates are stored in a domain entities index and is continuously updated with new entries so as to maintain the accuracy of the speech recognition of voice queries for recently created or recently available content.

Abstract: Some embodiments include a transcription knowledge graph that can resolve automatic speech recognition (ASR) engine output errors. In some embodiments, a transcription knowledge graph can utilize data from past sessions of the ASR engine to form a voice graph that can be analyzed to determine a correlation between a mis-transcription (error text) and the correct transcription (correct text). Thus, ASR engine outputs, even if they include a mis-transcription, can be adjusted to the correct transcription. Further, the correct transcriptions and the voice graph can be used to train machine learning (ML) algorithms to generate numerical representations of an entity. The ML algorithms can be applied to a transcription to correctly identify a corresponding entity label, even if the transcription was not utilized in the voice graph to train the ML algorithm.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and computer program product embodiments for adapting an automated speech recognition system to provide more accurate suggestions to voice queries involving media content including recently created or recently available content. An example computer-implemented method includes transcribing the voice query, identifying respective components of the query such as the media content being requested and the action to be performed, and generating fuzzy candidates that potentially match the media content based on phonetic representations of the identified components. Phonetic representations of domain specific candidates are stored in a domain entities index and is continuously updated with new entries so as to maintain the accuracy of the speech recognition of voice queries for recently created or recently available content.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and computer program product embodiments for adapting an automated speech recognition system to provide more accurate suggestions to voice queries involving media content including recently created or recently available content. An example computer-implemented method includes transcribing the voice query, identifying respective components of the query such as the media content being requested and the action to be performed, and generating fuzzy candidates that potentially match the media content based on phonetic representations of the identified components. Phonetic representations of domain specific candidates are stored in a domain entities index and is continuously updated with new entries so as to maintain the accuracy of the speech recognition of voice queries for recently created or recently available content.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and computer program product embodiments for adapting an automated speech recognition system to provide more accurate suggestions to voice queries involving media content including recently created or recently available content. An example computer-implemented method includes transcribing the voice query, identifying respective components of the query such as the media content being requested and the action to be performed, and generating fuzzy candidates that potentially match the media content based on phonetic representations of the identified components. Phonetic representations of domain specific candidates are stored in a domain entities index and is continuously updated with new entries so as to maintain the accuracy of the speech recognition of voice queries for recently created or recently available content.