SoundHound, Inc.

A method and system for controlling a GUI on a user's network-connected device, the control being provided by a telephone call between the user and a speech recognition and speech synthesis system. An example of a restaurant ordering system is provided. The user calls a phone number and is guided through a verbal ordering process that includes one or more of: adding an item, deleting an item, changing quantities, changing sizes, and changing details of an item. The user's choices are added to a display so that a current status of the order is visible to the user. The GUI is updated as changes are made to the order. The GUI can also request additional information, upsell items, and show menus. The GUI aids the user in confirming that the order is correct. The system provides the final order to a restaurant for fulfillment.

Methods and systems for correction of a likely erroneous word in a speech transcription are disclosed. By evaluating token confidence scores of individual words or phrases, the automatic speech recognition system can replace a low-confidence score word with a substitute word or phrase. Among various approaches, neural network models can be used to generate individual confidence scores. Such word substitution can enable the speech recognition system to automatically detect and correct likely errors in transcription. Furthermore, the system can indicate the token confidence scores on a graphic user interface for labeling and dictionary enhancement.

A computer-implemented method is provided. The method including receiving speech audio of dictation associated with a user ID, deriving acoustic features from the speech audio, storing the derived acoustic features in a user profile associated with the user ID, receiving a request for acoustic features through an application programming interface (API), the request including the user ID, and sending the derived acoustic features through the API.

A computer system ingests a catalog of a plurality of items. The catalog is specific to a particular domain and including names for individual items of the plurality of items. One or more attributes are respectively associated to the individual items of the plurality of items. A specialist grammar specific to the particular domain of the catalog is obtained and a programming language code to interpret natural language input related to the catalog is generated using the specialist grammar, and the names for the individual items of the plurality of items and their associated one or more attributes.

An audio recognition system provides for delivery of promotional content to its user. A user interface device, locally or with the assistance of a network-connected server, performs recognition of audio in response to queries. Recognition can be through a method such as processing features extracted from the audio. Audio can comprise recorded music, singing or humming, instrumental music, vocal music, spoken voice, or other recognizable types of audio. Campaign managers provide promotional content for delivery in response to audio recognized in queries.

A method includes recognizing words comprised by a first utterance; interpreting the recognized words according to a grammar comprised by a domain; from the interpreting of the recognized words, determining a timeout period for the first utterance based on the domain of the first utterance; detecting end of voice activity in the first utterance; executing an instruction following an amount of time after detecting end of voice activity of the first utterance in response to the amount of time exceeding the timeout period, the executed instruction based at least in part on interpreting the recognized words.

As audio (1) is input to an extension of a browser, the extension transmits the audio (1) to a language processing server. A speech recognition unit obtains a text (1) corresponding to the audio (1), and transmits the text (1) to a natural language understanding unit. In the natural language understanding unit, an information processing unit identifies a URL (1) corresponding to the text (1), and transmits the URL (1) to the browser. The extension passes the URL (1) to a browsing function. The browsing function uses the URL (1) to access a web server. The web server transmits a web page (1) corresponding to the URL (1) to the browser. The browsing function shows a screen corresponding to the web page (1) on a display.

A system and method of real-time feedback confirmation to solicit a virtual assistant response from an evolving semantic state of at least a portion of an utterance. A user accesses a virtual assistant on an electronic device having the system and/or method configured to capture a command, a question, and/or a fulfillment request from audio such as, the speech emitted from the speaking user. The speech may be intercepted by a speech engine configured to transcribe the speech into text that is matched with the fragment pattern's regular expression to generate a fragment and/or the speech may be processed with a machine learning model to identify fragments. The fragments are identified by a domain handler configured to update a data structure of the current semantic state of the utterance in real-time on an interface of an electronic device.

Automatically generating sentences that a user can say to invoke a set of defined actions performed by a virtual assistant are disclosed. A sentence is received and keywords are extracted from the sentence. Based on the keywords, additional sentences are generated. A classifier model is applied to the generated sentences to determine a sentence that satisfies a threshold. In the situation a sentence satisfies the threshold, an intent associated with the classifier model can be invoked. In the situation the sentences fail to satisfy the classifier model, the virtual assistant can attempt to interpret the received sentence according to the most likely intent by invoking a sentence generation model fine-tuned for a particular domain, generate additional sentences with a high probability of having the same intent and fulfill the specific action defined by the intent.

A neural TTS system is trained to generate key acoustic frames at variable rates while omitting other frames. The frame skipping depends on the acoustic features to be generated for the input text. The TTS system can interpolate frames between the key frames at a target rate for a vocoder to synthesis audio samples.