Abstract: An automatic speech recognition engine may generate text or tokens that correspond to audio data. For example, the automatic speech recognition engine may generate first text or first speech tokens corresponding to a first portion of audio data. The automatic speech recognition engine may further generate second text or second speech tokens that correspond to a first portion of the audio data and a second portion of the audio data. The text or speech tokens generated by the automatic speech recognition engine may be provided to a device for presentation thereon. In some embodiments, the automatic speech recognition engine generates the second text or second speech tokens substantially while the first text or first speech tokens are presented on the device.

Abstract: A method for producing speech recognition results on a device includes receiving first speech recognition results, obtaining a language model, wherein the language model represents information stored on the device, and using the first speech recognition results and the language model to generate second speech recognition results.

Abstract: Provided are systems and methods for using hierarchical networks for recognition, such as speech recognition. Conventional automatic recognition systems may not be both efficient and flexible. Recognition systems are disclosed that may achieve efficiency and flexibility by employing hierarchical networks, prefix consolidation of networks, and future consolidation of networks. The disclosed networks may be associated with a network model and the associated network model may be modified during recognition to achieve greater flexibility.

Abstract: Targeted delivery of contextually relevant ad impressions to a mobile device is provided. The ad impressions are delivered within text messages and/or instant message chat threads. Monetizing of text messaging and instant messaging by providers of such services is achieved, while providing unobtrusive and contextually relevant information to users of such services.

Abstract: A device may include a physical phenomenon detector. The physical phenomenon detector may detect a physical phenomenon related to the device. In response to detecting the physical phenomenon, the device may record audio data that includes speech. The speech may be transcribed with a speech recognition engine. The speech recognition engine may be included in the device, or may be included with a remote computing device with which the device may communicate.

Abstract: Methods, systems, and software for converting the audio input of a user of a hand-held client device or mobile phone into a textual representation by means of a backend server accessed by the device through a communications network. The text is then inserted into or used by an application of the client device to send a text message, instant message, email, or to insert a request into a web-based application or service. In one embodiment, the method includes the steps of initializing or launching the application on the device; recording and transmitting the recorded audio message from the client device to the backend server through a client-server communication protocol; converting the transmitted audio message into the textual representation in the backend server; and sending the converted text message back to the client device or forwarding it on to an alternate destination directly from the server.

Abstract: Provided are systems and methods for using hierarchical networks for recognition, such as speech recognition. Conventional automatic recognition systems may not be both efficient and flexible. Recognition systems are disclosed that may achieve efficiency and flexibility by employing hierarchical networks, prefix consolidation of networks, and future consolidation of networks. The disclosed networks may be associated with a network model and the associated network model may be modified during recognition to achieve greater flexibility.

Abstract: A method for transcribing a spoken communication includes acts of receiving a spoken first communication from a first sender to a first recipient, obtaining information relating to a second communication, which is different from the first communication, from a second sender to a second recipient, using the obtained information to obtain a language model, and using the language model to transcribe the spoken first communication.

Abstract: A method of providing speech transcription performance indication includes receiving, at a user device data representing text transcribed from an audio stream by an ASR system, and data representing a metric associated with the audio stream; displaying, via the user device, said text; and via the user device, providing, in user-perceptible form, an indicator of said metric. Another method includes displaying, by a user device, text transcribed from an audio stream by an ASR system; and via the user device, providing, in user-perceptible form, an indicator of a level of background noise of the audio stream. Another method includes receiving data representing an audio stream; converting said data representing an audio stream to text via an ASR system; determining a metric associated with the audio stream; transmitting data representing said text to a user device; and transmitting data representing said metric to the user device.

Abstract: A method for presenting additional content for a word that is part of a message, and that is presented by a mobile communication device, includes the steps of: presenting the message, including emphasizing one or more words for which respective additional content is available for presenting by the mobile communication device; receiving an utterance that includes an emphasized word for which additional content is available for presenting by the mobile communication device; and presenting the additional content for the emphasized word included in the utterance received by the mobile communication device. These steps are performed by the mobile communication device.

Abstract: Audio data that includes speech may be transcribed using a language model. The transcription may be provided to a user. The user may provide feedback on the transcription, and the language model may be updated based at least in part on the feedback. The feedback may include, for example, an affirmation of the transcription; a disapproval of the transcription; a correction to the transcription; a selection of an alternate transcription result; or any other kind of response.

Abstract: A method for facilitating mobile phone messaging, such as text messaging and instant messaging, includes receiving audio data communicated from the mobile communication device, the audio data representing an utterance that is intended to be at least a portion of the text of the message that is to be sent from the mobile phone to a recipient; transcribing the utterance to text based on the received audio data to generate a transcription; and applying a filter to the transcribed text to generate a filtered transcription, the text of which is intended to mimic language patterns of mobile device messaging that is performed manually by users. The method may also be applied to the audio data of a voicemail, with the filtered, transcribed text being communicated to a mobile phone as, for example, an SMS text message.

Abstract: A system and method of validating an advertisement presented to an advertisement recipient via a mobile communication device includes presenting an advertisement for a product or service to a recipient via a mobile communication device, monitoring the geospatial location of the mobile communication device relative to some predetermined criteria, and inferring information about the reaction of the advertisement recipient to the advertisement on the basis of the monitored geospatial location information.

Abstract: A method of utilizing metadata stored in a computer-readable medium to assist in the conversion of an audio stream to a text stream. The method compares personally identifiable data, such as a user's electronic address book and/or Caller/Recipient ID information (in the case of processing voice mail to text), to the n-best results generated by a speech recognition engine for each word that is output by the engine. A goal of this comparison is to correct a possible misrecognition of a spoken proper noun such as a name or company with its proper textual form or a spoken phone number to correctly formatted phone number with Arabic numerals to improve the overall accuracy of the output of the voice recognition system.

Abstract: A system and method of validating an advertisement presented to an advertisement recipient via a mobile communication device includes presenting an advertisement for a product or service to a recipient via a mobile communication device, monitoring the geospatial location of the mobile communication device relative to some predetermined criteria, and inferring information about the reaction of the advertisement recipient to the advertisement on the basis of the monitored geospatial location information.

Abstract: An automatic speech recognition engine may generate text or tokens that correspond to audio data. For example, the automatic speech recognition engine may generate first text or first speech tokens corresponding to a first portion of audio data. The automatic speech recognition engine may further generate second text or second speech tokens that correspond to a first portion of the audio data and a second portion of the audio data. The text or speech tokens generated by the automatic speech recognition engine may be provided to a device for presentation thereon. In some embodiments, the automatic speech recognition engine generates the second text or second speech tokens substantially while the first text or first speech tokens are presented on the device.

Abstract: Audio data that includes speech may be transcribed to text by a speech recognition engine. One or more metrics associated with the audio data and/or the text may be determined. An indicator related to a metric may be provided for a portion of the audio data or the text for which the metric was determined. The indicator may be presented in a user-perceptible format.

Abstract: A method of providing speech transcription performance indication includes receiving, at a user device data representing text transcribed from an audio stream by an ASR system, and data representing a metric associated with the audio stream; displaying, via the user device, said text; and via the user device, providing, in user-perceptible form, an indicator of said metric. Another method includes displaying, by a user device, text transcribed from an audio stream by an ASR system; and via the user device, providing, in user-perceptible form, an indicator of a level of background noise of the audio stream. Another method includes receiving data representing an audio stream; converting said data representing an audio stream to text via an ASR system; determining a metric associated with the audio stream; transmitting data representing said text to a user device; and transmitting data representing said metric to the user device.

Abstract: Audio data that includes speech may be transcribed by a speech recognition engine to generate speech recognition results, such as a transcription. One or more filters may be selected and applied to the speech recognition results to generate filtered speech recognition results. The one or more filters may be selected based at least in part on a characteristic of the speech recognition results, a characteristic of the audio data, or any other characteristic.

Abstract: Methods, systems, and software for converting the audio input of a user of a hand-held client device or mobile phone into a textual representation by means of a backend server accessed by the device through a communications network. The text is then inserted into or used by an application of the client device to send a text message, instant message, email, or to insert a request into a web-based application or service. In one embodiment, the method includes the steps of initializing or launching the application on the device; recording and transmitting the recorded audio message from the client device to the backend server through a client-server communication protocol; converting the transmitted audio message into the textual representation in the backend server; and sending the converted text message back to the client device or forwarding it on to an alternate destination directly from the server.