Abstract: Voice data from a current conversation between a user and a voice-controlled user device can be used to determine a search constraint for searching a database. Other search constraints can be determined based at least in part on the current conversation, a previous conversation, and/or a previous action. Properties can be associated with the search constraints. Once the search constraints have been determined, a plurality of search query plans is determined and a first search query plan is executed to query the database.

Abstract: Dialog acts (e.g., questions) are selected for voice browsing by a machine learning model trained to identify a dialog act that is most likely to lead to a desired outcome. When an invocation is received from a user, a context of the invocation is determined, and a pool of dialog acts is scored based on the context by a machine learning model. Dialog acts are selected from the pool and presented to the user in accordance with a randomization policy. Data regarding the dialog acts and their success in achieving a desired outcome is used to train one or more machine learning models to select dialog acts in response to invocations.

Abstract: Automatic speech recognition (ASR) processing including a feedback configuration to allow for improved disambiguation between ASR hypotheses. After ASR processing of an incoming utterance where the ASR outputs an N-best list including multiple hypotheses, the multiple hypotheses are passed downstream for further processing. The downstream further processing may include natural language understanding (NLU) or other processing to determine a command result for each hypothesis. The command results are compared to determine if any hypotheses of the N-best list would yield similar command results. If so, the hypothesis(es) with similar results are removed from the N-best list so that only one hypothesis of the similar results remains in the N-best list. The remaining non-similar hypotheses are sent for disambiguation, or, if only one hypothesis remains, it is sent for execution.

Abstract: Automatic speech recognition (ASR) processing including a feedback configuration to allow for improved disambiguation between ASR hypotheses. After ASR processing of an incoming utterance where the ASR outputs an N-best list including multiple hypotheses, the multiple hypotheses are passed downstream for further processing. The downstream further processing may include natural language understanding (NLU) or other processing to determine a command result for each hypothesis. The command results are compared to determine if any hypotheses of the N-best list would yield similar command results. If so, the hypothesis(es) with similar results are removed from the N-best list so that only one hypothesis of the similar results remains in the N-best list. The remaining non-similar hypotheses are sent for disambiguation, or, if only one hypothesis remains, it is sent for execution.

Abstract: Automatic speech recognition (ASR) processing including a two-stage configuration. After ASR processing of an incoming utterance where the ASR outputs an N-best list including multiple hypotheses, a first stage determines whether to execute a command associated with one of the hypotheses or whether to output some of the hypotheses of the N-best list for disambiguation. A second stage determines what hypotheses should be included in the disambiguation choices. A first machine learning model is used at the first stage and a second machine learning model is used at the second stage. The multi-stage configuration allows for reduced speech processing errors as well as a reduced number of utterances sent for disambiguation, which thus improves the user experience.

Abstract: An apparatus for utilizing textual data and acoustic data corresponding to speech data to detect sentiment may include a processor and memory storing executable computer code causing the apparatus to at least perform operations including evaluating textual data and acoustic data corresponding to voice data associated with captured speech content. The computer program code may further cause the apparatus to analyze the textual data and the acoustic data to detect whether the textual data or the acoustic data includes one or more words indicating at least one sentiment of a user that spoke the speech content. The computer program code may further cause the apparatus to assign at least one predefined sentiment to at least one of the words in response to detecting that the word(s) indicates the sentiment of the user. Corresponding methods and computer program products are also provided.

Abstract: An apparatus for generating a review based in part on detected sentiment may include a processor and memory storing executable computer code causing the apparatus to at least perform operations including determining a location(s) of the apparatus and a time(s) that the location(s) was determined responsive to capturing voice data of speech content associated with spoken reviews of entities. The computer program code may further cause the apparatus to analyze textual and acoustic data corresponding to the voice data to detect whether the textual or acoustic data includes words indicating a sentiment(s) of a user speaking the speech content. The computer program code may further cause the apparatus to generate a review of an entity corresponding to a spoken review(s) based on assigning a predefined sentiment to a word(s) responsive to detecting that the word indicates the sentiment of the user. Corresponding methods and computer program products are also provided.

Abstract: An apparatus for generating a review based in part on detected sentiment may include a processor and memory storing executable computer code causing the apparatus to at least perform operations including determining a location(s) of the apparatus and a time(s) that the location(s) was determined responsive to capturing voice data of speech content associated with spoken reviews of entities. The computer program code may further cause the apparatus to analyze textual and acoustic data corresponding to the voice data to detect whether the textual or acoustic data includes words indicating a sentiment(s) of a user speaking the speech content. The computer program code may further cause the apparatus to generate a review of an entity corresponding to a spoken review(s) based on assigning a predefined sentiment to a word(s) responsive to detecting that the word indicates the sentiment of the user. Corresponding methods and computer program products are also provided.

Abstract: An apparatus for utilizing textual data and acoustic data corresponding to speech data to detect sentiment may include a processor and memory storing executable computer code causing the apparatus to at least perform operations including evaluating textual data and acoustic data corresponding to voice data associated with captured speech content. The computer program code may further cause the apparatus to analyze the textual data and the acoustic data to detect whether the textual data or the acoustic data includes one or more words indicating at least one sentiment of a user that spoke the speech content. The computer program code may further cause the apparatus to assign at least one predefined sentiment to at least one of the words in response to detecting that the word(s) indicates the sentiment of the user. Corresponding methods and computer program products are also provided.