Abstract: Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

Abstract: Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

Abstract: Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

Abstract: Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

Abstract: Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

Abstract: Content items and other entities may be ranked or organized according to a relevance to a user. Relevance may take into consideration recency, proximity, popularity, air time (e.g., of television shows) and the like. In one example, the popularity and age of a movie may be used to determine a relevance ranking. Popularity (i.e., entity rank) may be determined based on a variety of factors. In the movie example, popularity may be based on gross earnings, awards, nominations, votes and the like. According to one or more embodiments, entities may initially be categorized into relevance groupings based on popularity and/or other factors. Once categorized, the entities may be sorted within each grouping and later combined into a single ranked list.

Abstract: Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

Abstract: Content items and other entities may be ranked or organized according to a relevance to a user. Relevance may take into consideration recency, proximity, popularity, air time (e.g., of television shows) and the like. In one example, the popularity and age of a movie may be used to determine a relevance ranking. Popularity (i.e., entity rank) may be determined based on a variety of factors. In the movie example, popularity may be based on gross earnings, awards, nominations, votes and the like. According to one or more embodiments, entities may initially be categorized into relevance groupings based on popularity and/or other factors. Once categorized, the entities may be sorted within each grouping and later combined into a single ranked list.

Abstract: Content items and other entities may be ranked or organized according to a relevance to a user. Relevance may take into consideration recency, proximity, popularity, air time (e.g., of television shows) and the like. In one example, the popularity and age of a movie may be used to determine a relevance ranking Popularity (i.e., entity rank) may be determined based on a variety of factors. In the movie example, popularity may be based on gross earnings, awards, nominations, votes and the like. According to one or more embodiments, entities may initially be categorized into relevance groupings based on popularity and/or other factors. Once categorized, the entities may be sorted within each grouping and later combined into a single ranked list.

Abstract: Content items and other entities may be ranked or organized according to a relevance to a user. Relevance may take into consideration recency, proximity, popularity, air time (e.g., of television shows) and the like. In one example, the popularity and age of a movie may be used to determine a relevance ranking Popularity (i.e., entity rank) may be determined based on a variety of factors. In the movie example, popularity may be based on gross earnings, awards, nominations, votes and the like. According to one or more embodiments, entities may initially be categorized into relevance groupings based on popularity and/or other factors. Once categorized, the entities may be sorted within each grouping and later combined into a single ranked list.

Abstract: Content items and other entities may be ranked or organized according to a relevance to a user. Relevance may take into consideration recency, proximity, popularity, air time (e.g., of television shows) and the like. In one example, the popularity and age of a movie may be used to determine a relevance ranking. Popularity (i.e., entity rank) may be determined based on a variety of factors. In the movie example, popularity may be based on gross earnings, awards, nominations, votes and the like. According to one or more embodiments, entities may initially be categorized into relevance groupings based on popularity and/or other factors. Once categorized, the entities may be sorted within each grouping and later combined into a single ranked list.

Abstract: Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

Abstract: Content items and other entities may be ranked or organized according to a relevance to a user. Relevance may take into consideration recency, proximity, popularity, air time (e.g., of television shows) and the like. In one example, the popularity and age of a movie may be used to determine a relevance ranking. Popularity (i.e., entity rank) may be determined based on a variety of factors. In the movie example, popularity may be based on gross earnings, awards, nominations, votes and the like. According to one or more embodiments, entities may initially be categorized into relevance groupings based on popularity and/or other factors. Once categorized, the entities may be sorted within each grouping and later combined into a single ranked list.