Abstract: A processing system receives an audio signal encoding a portion of an utterance. The processing system receives context information associated with the utterance, wherein the context information is not derived from the audio signal or any other audio signal. The processing system provides, as input to a neural network, data corresponding to the audio signal and the context information, and generates a transcription for the utterance based on at least an output of the neural network.

Abstract: The present disclosure relates to training a speech recognition system. One example method includes receiving a collection of speech data items, wherein each speech data item corresponds to an utterance that was previously submitted for transcription by a production speech recognizer. The production speech recognizer uses initial production speech recognizer components in generating transcriptions of speech data items. A transcription for each speech data item is generated using an offline speech recognizer, and the offline speech recognizer components are configured to improve speech recognition accuracy in comparison with the initial production speech recognizer components. The updated production speech recognizer components are trained for the production speech recognizer using a selected subset of the transcriptions of the speech data items generated by the offline speech recognizer.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for caching speech recognition scores. In some implementations, one or more values comprising data about an utterance are received. An index value is determined for the one or more values. An acoustic model score for the one or more received values is selected, from a cache of acoustic model scores that were computed before receiving the one or more values, based on the index value. A transcription for the utterance is determined using the selected acoustic model score.

Abstract: Examples of methods and systems for implementing environmentally aware speech recognition are described. In some examples, a method may be performed by a computing device within a system to adapt an acoustic model for a particular language to one or more environmental conditions. A device may receive one or more spoken utterances and based on the utterances, a system containing the device may determine an acoustic model for the particular language. The system may adapt the acoustic model using one or more data sets depending on the environmental conditions at the location of the device or may obtain another acoustic model that is adapted to the environmental conditions. In some examples, the system may also adapt the acoustic model using one or more data sets based on the voice characteristics of the speaker of the one or more spoken utterances.

Abstract: This document describes methods, systems, techniques, and computer program products for generating and/or modifying acoustic models. Acoustic models and/or transformations for a target language/dialect can be generated and/or modified using acoustic models and/or transformations from a source language/dialect.

Abstract: Characteristics of a speaker are estimated using speech processing and machine learning. The characteristics of the speaker are used to automatically customize a user interface of a client device for the speaker.

Abstract: Systems and methods for identifying music in a noisy environment are described. One of the methods includes receiving audio segment data. The audio segment data is generated from the portion that is captured in the noisy environment. The method further includes generating feature vectors from the audio segment data, identifying phonemes from the feature vectors, and comparing the identified phonemes with pre-assigned phoneme sequences. Each pre-assigned phoneme sequence identifies a known music piece. The method further includes determining an identity of the music based on the comparison.

Abstract: A processing system receives an audio signal encoding a portion of an utterance. The processing system receives context information associated with the utterance, wherein the context information is not derived from the audio signal or any other audio signal. The processing system provides, as input to a neural network, data corresponding to the audio signal and the context information, and generates a transcription for the utterance based on at least an output of the neural network.

Abstract: Methods and systems for speech recognition processing are described. In an example, a computing device may be configured to receive information indicative of a frequency of submission of a search query to a search engine for a search query composed of a sequence of words. Based on the frequency of submission of the search query exceeding a threshold, the computing device may be configured to determine groupings of one or more words of the search query based on an order in which the one or more words occur in the sequence of words of the search query. Further, the computing device may be configured to provide information indicating the groupings to a speech recognition system.

Abstract: Methods and systems for speech recognition processing are described. In an example, a computing device may be configured to receive information indicative of a frequency of submission of a search query to a search engine for a search query composed of a sequence of words. Based on the frequency of submission of the search query exceeding a threshold, the computing device may be configured to determine groupings of one or more words of the search query based on an order in which the one or more words occur in the sequence of words of the search query. Further, the computing device may be configured to provide information indicating the groupings to a speech recognition system.

Abstract: This document generally describes computer technologies relating to generating speech data collection prompts, such as textual scripts and/or textual scenarios. Speech data collection prompts for a particular language can be generated based on a variety of factors, including the frequency with which linguistic elements (e.g., phonemes, syllables, words, phrases) in the particular language occur in one or more corpora of textual information associated with the particular language. Textual prompts can also and/or alternatively be generated based on statistics for previously recorded speech data.

Abstract: Disclosed are apparatus and methods for processing spoken speech. Input speech can be received at a computing system. During a first pass of speech recognition, a plurality of language model outputs can be determined by: providing the input speech to each of a plurality of language models and responsively receiving a language model output from each language model. A language model of the plurality of language models can be selected using a classifier operating on the plurality of language model outputs. During a second pass of speech recognition, a revised language model output can be determined by: providing the input speech and the language model output from the selected language model to the selected language model and responsively receiving the revised language model output from the selected language model. The computing system can generate a result based on the revised language model output.

Abstract: Methods and systems for speech recognition processing are described. In an example, a computing device may be configured to receive information indicative of a frequency of submission of a search query to a search engine for a search query composed of a sequence of words. Based on the frequency of submission of the search query exceeding a threshold, the computing device may be configured to determine groupings of one or more words of the search query based on an order in which the one or more words occur in the sequence of words of the search query. Further, the computing device may be configured to provide information indicating the groupings to a speech recognition system.

Abstract: This document describes methods, systems, techniques, and computer program products for generating and/or modifying acoustic models. Acoustic models and/or transformations for a target language/dialect can be generated and/or modified using acoustic models and/or transformations from a source language/dialect.

Abstract: Examples of methods and systems for providing speech recognition systems based on speech recordings logs are described. In some examples, a method may be performed by a computing device within a system to generate modified data logs to use as a training data set for an acoustic model for a particular language. A device may receive one or more data logs that comprise at least one or more recordings of spoken queries and transcribe the recordings. Based on comparisons, the device may identify any transcriptions that may be indicative of noise and may remove those transcriptions indicative of noise from the data logs. Further, the device may remove unwanted transcriptions from the data logs and the device may provide the modified data logs as a training data set to one or more acoustic models for particular languages.

Abstract: This document describes methods, systems, techniques, and computer program products for generating and/or modifying acoustic models. Acoustic models and/or transformations for a target language/dialect can be generated and/or modified using acoustic models and/or transformations from a source language/dialect.

Abstract: This document describes methods, systems, techniques, and computer program products for generating and/or modifying acoustic models. Acoustic models and/or transformations for a target language/dialect can be generated and/or modified using acoustic models and/or transformations from a source language/dialect.