Abstract: Route information data records are received from a navigation system indicating road elements and maneuvers between a starting point and a destination point. An estimated time of arrival is established using the route information data records. A personalized correction value based on the road elements, maneuvers, and a driver profile is determined. A personalized time of arrival is calculated based on the estimated time of arrival value and the personalized correction value.

Abstract: A plurality of instances of probe data are received and used to generate a plurality of probe trajectories. A set of selected probe trajectories is defined using spatial familiarity sampling wherein a familiarity score is determined for each instance of probe data of a probe trajectory based on a familiarity model corresponding to already selected probe trajectories. A least familiar familiarity score for a probe trajectory is identified from the familiarity scores determined for the probe trajectory and a determination of whether the probe trajectory is included in the set of selected probe trajectories is performed based on the least familiar familiarity score for the probe trajectory. Only the probe trajectories of the set of selected probe trajectories are map-matched. The map-matched probe trajectories are then used to generate traffic and/or map information/data which is provided to consumer apparatuses for use in performing navigation functions, for example.

Abstract: An apparatus including a processor and memory having computer program code, the memory and computer program code configured to, with the processor, enable the apparatus at least to: use a classification model to identify one or more anomalies between a monitored trajectory of a vehicle through a road network and predefined map data for the road network which are likely caused by errors in the predefined map data; and provide details of the one or more identified anomalies for use in obtaining additional data for the specific locations on the road network corresponding to the one or more identified anomalies in order to correct the errors in the predefined map data.

Abstract: A plurality of instances of probe data are received and used to generate a plurality of probe trajectories. A set of selected probe trajectories is defined using spatial familiarity sampling wherein a familiarity score is determined for each instance of probe data of a probe trajectory based on a familiarity model corresponding to already selected probe trajectories. A least familiar familiarity score for a probe trajectory is identified from the familiarity scores determined for the probe trajectory and a determination of whether the probe trajectory is included in the set of selected probe trajectories is performed based on the least familiar familiarity score for the probe trajectory. Only the probe trajectories of the set of selected probe trajectories are map-matched. The map-matched probe trajectories are then used to generate traffic and/or map information/data which is provided to consumer apparatuses for use in performing navigation functions, for example.

Abstract: A method, apparatus, and computer program product are therefore provided for disambiguating non-deterministic spoken dialog commands as they relate to points-of-interest and information regarding points-of-interest within or near the field of view of a user. An apparatus may include a processor and a memory including program code instructions. The program code instructions are configured to, when executed, cause the apparatus to at least: receive an indication of a user location; receive an indication of a field of view of the user at the location; receive a natural language utterance; generate, from the natural language utterance, a query, where the query includes at least one request determined from the natural language utterance, the location, and the field of view of the user; determine a point-of-interest associated with the query by conducting a search of a database using the generated query; and provide information associated with the point-of-interest via a user interface.

Abstract: An approach is provided for mobility-based language model adaptation for navigational speech interfaces. The approach involves receiving a word lattice resulting from a speech recognition process applied on a speech input received from a user via a navigational speech interface. The word lattice includes a respective speech recognition score for each word in the word lattice. The approach further involves adapting a language model comprising a plurality of location entities according to a mobility relevance and a search relevance of the plurality of location entities to the user. In one embodiment, the mobility relevance and the search relevance are determined by computing a probability of the user visiting the location entities associated with said each word after the user queries for said each word. The approach further involves initiating a re-scoring of said each word in the word lattice using the adapted language model to generate a speech recognition output.

Abstract: A method, apparatus, and computer program product are therefore provided for disambiguating non-deterministic spoken dialog commands as they relate to points-of-interest and information regarding points-of-interest within or near the field of view of a user. An apparatus may include a processor and a memory including program code instructions.

Abstract: Methods, apparatus, systems, and computer program products are provided for providing distributed online learning for personal predictive models that preserves user privacy. An example method comprises receiving model updates based at least in part on synchronization metadata. The model update comprises one or more received model parameter updates. The received model parameter updates correspond to one or more model parameters. The model updates are associated with a particular apparatus. The example method further comprises determining a decay factor based at least in part on the synchronization metadata; applying the decay factor to the one or more received model parameter updates; and updating the one or more model parameters based on the decayed model parameter updates.

Abstract: Route information data records are received from a navigation system indicating road elements and maneuvers between a starting point and a destination point. An estimated time of arrival is established using the route information data records. A personalized correction value based on the road elements, maneuvers, and a driver profile is determined. A personalized time of arrival is calculated based on the estimated time of arrival value and the personalized correction value.

Abstract: An approach is provided for global voice-based entry of location information. The approach involves partitioning a global speech decoding graph into spatial partitions. The approach also involves determining key entities occurring in each spatial partitions to construct a combined set of key entities. The approach further involves creating a retrieval index to map the key entities in the combined set of key entities to a corresponding partition. A first partition, the combined set of key entities, and the retrieved index are stored in a memory of a device for processing a voice input signal. A second partition that is not in the memory of the device is retrieved based on the combined set of key entities and the retrieval index to automatically re-process the voice input signal when an out-of-vocabulary result is obtained from the first partition.

Abstract: Route information data records are received from a navigation system indicating road elements and maneuvers between a starting point and a destination point. An estimated time of arrival is established using the route information data records. A personalized correction value based on the road elements, maneuvers, and a driver profile is determined. A personalized time of arrival is calculated based on the estimated time of arrival value and the personalized correction value.

Abstract: An approach is provided for mobility-based language model adaptation for navigational speech interfaces. The approach involves receiving a word lattice resulting from a speech recognition process applied on a speech input received from a user via a navigational speech interface. The word lattice includes a respective speech recognition score for each word in the word lattice. The approach further involves adapting a language model comprising a plurality of location entities according to a mobility relevance and a search relevance of the plurality of location entities to the user. In one embodiment, the mobility relevance and the search relevance are determined by computing a probability of the user visiting the location entities associated with said each word after the user queries for said each word. The approach further involves initiating a re-scoring of said each word in the word lattice using the adapted language model to generate a speech recognition output.

Abstract: An apparatus comprising a processor and memory including computer program code, the memory and computer program code configured to, with the processor, enable the apparatus at least to: use a classification model to identify one or more anomalies between a monitored trajectory of a vehicle through a road network and predefined map data for the road network which are likely caused by errors in the predefined map data; and provide details of the one or more identified anomalies for use in obtaining additional data for the specific locations on the road network corresponding to the one or more identified anomalies in order to correct the errors in the predefined map data.

Abstract: An approach is provided for global voice-based entry of location information. The approach involves partitioning a global speech decoding graph into spatial partitions. The approach also involves determining key entities occurring in each spatial partitions to construct a combined set of key entities. The approach further involves creating a retrieval index to map the key entities in the combined set of key entities to a corresponding partition. A first partition, the combined set of key entities, and the retrieved index are stored in a memory of a device for processing a voice input signal. A second partition that is not in the memory of the device is retrieved based on the combined set of key entities and the retrieval index to automatically re-process the voice input signal when an out-of-vocabulary result is obtained from the first partition.

Abstract: Methods, apparatus, systems, and computer program products are provided for providing distributed online learning for personal predictive models that preserves user privacy. An example method comprises receiving model updates based at least in part on synchronization metadata. The model update comprises one or more received model parameter updates. The received model parameter updates correspond to one or more model parameters. The model updates are associated with a particular apparatus. The example method further comprises determining a decay factor based at least in part on the synchronization metadata; applying the decay factor to the one or more received model parameter updates; and updating the one or more model parameters based on the decayed model parameter updates.

Abstract: Route information data records are received from a navigation system indicating road elements and maneuvers between a starting point and a destination point. An estimated time of arrival is established using the route information data records. A personalized correction value based on the road elements, maneuvers, and a driver profile is determined. A personalized time of arrival is calculated based on the estimated time of arrival value and the personalized correction value.