Abstract: A user device may detect speech and use “early exiting” when identifying a potential operational command in the detected speech. The implementation of early exiting may be based on a variable threshold, where variable sensitivity settings for the threshold may be used to control how quickly, and whether, an “early exit” or early prediction of an operational command will occur. An early exit threshold may be adjusted, for example, based on network conditions, to ensure optimal operational command determination from the audio.

Abstract: Methods may include receiving, via a computing device, a query. Methods may include causing, based on the query, input of a first prompt to a first model. Methods may furthermore include receiving, via the first model, a first output. Methods may include determining, based on the first output, a confidence score. Methods may include determining, based on a comparison of a first performance of the first model and a second performance of a second model, a threshold. Methods may include causing, based on the confidence score not satisfying the threshold, input of a second prompt to the second model. The second prompt may be based at least on the query. Methods may include receiving, via the second model, a second output. Methods may include causing, based on the query, the second output to be output via the computing device.

Abstract: Systems, apparatuses, and methods are described for minimizing prompt order bias in a large language model (LLM). Using an original input prompt for an LLM, that may include instructions and ordered list, a plurality of different LLM input prompts may be generated. A plurality of LLM outputs may be determined, for example, by providing the plurality of LLM input prompts comprising the original instructions but with the order of the list permutated. A positional bias of the LLM may appear differently in the plurality of LLM outputs, for example, based on the differing list orders of the plurality of LLMs. A final LLM output may be generated, for example, by aggregating the LLM outputs to minimize the effects of positional bias.

Abstract: First audio data associated with a first portion of a voice query (e.g., an incomplete voice query) may be received (e.g., by a device or a server). A first transcript may be determined by a speech recognition engine and based on the first audio data. A plurality of predicted queries may be determined by applying a prediction process to the first transcript. A response for each of the plurality of predicted queries may be determined by processing the plurality of the predicted queries. Second audio data associated with a second portion of the voice query (e.g., a complete voice query) may be received. A second transcript may be determined by the speech recognition engine and based on the second audio data. Based on comparing the second transcript to one of the plurality of predicted queries, a response for the voice query may be returned.

Abstract: First audio data associated with a first portion of a voice query (e.g., an incomplete voice query) may be received (e.g., by a device or a server). A first transcript may be determined by a speech recognition engine and based on the first audio data. A plurality of predicted queries may be determined by applying a prediction process to the first transcript. A response for each of the plurality of predicted queries may be determined by processing the plurality of the predicted queries. Second audio data associated with a second portion of the voice query (e.g., a complete voice query) may be received. A second transcript may be determined by the speech recognition engine and based on the second audio data. Based on comparing the second transcript to one of the plurality of predicted queries, a response for the voice query may be returned.

Abstract: Methods and systems for improved data processing are disclosed. Data associated with a plurality of voice queries, having different length or time durations, may be received for processing. The plurality of voice queries may comprise a first voice query that has a first length. Based on the first length of the received data, a first processing function of a plurality of processing functions, and/or a server of a plurality of servers may be determined for further processing. The first processing function may be configured to process voice queries associated with the first length. The data associated with the first voice query may be sent for processing by the first processing function to determine text associated with the first voice query.

Abstract: Selection of training utterances may be carried out in a sample-efficient manner, and the selected training utterances may be annotated to provide improved training information to an ASR system. A computing device may receive, from an ASR system, one or more transcript-score pairs, wherein a transcript-score pair comprises a transcription associated with a voice query and at least one score associated with the transcription. The computing device may determine a likelihood of a word error associated with each transcription of the one or more transcript-score pairs. The computing device may determine, based on the likelihood of the word error, an effect on a word-error rate of the ASR system. The computing device may send at least one of the one or more transcript-score pairs with a threshold effect on the word-error rate of the ASR system to be annotated.

Abstract: A user device may detect speech and use “early exiting” when identifying a potential operational command in the detected speech. The implementation of early exiting may be based on a variable threshold, where variable sensitivity settings for the threshold may be used to control how quickly, and whether, an “early exit” or early prediction of an operational command will occur. An early exit threshold may be adjusted, for example, based on network conditions, to ensure optimal operational command determination from the audio.

Abstract: First audio data associated with a first portion of a voice query (e.g., an incomplete voice query) may be received (e.g., by a device or a server). A first transcript may be determined by a speech recognition engine and based on the first audio data. A plurality of predicted queries may be determined by applying a prediction process to the first transcript. A response for each of the plurality of predicted queries may be determined by processing the plurality of the predicted queries. Second audio data associated with a second portion of the voice query (e.g., a complete voice query) may be received. A second transcript may be determined by the speech recognition engine and based on the second audio data. Based on comparing the second transcript to one of the plurality of predicted queries, a response for the voice query may be returned.