Abstract: In some examples, a method is disclosed. The method includes detecting, by a smart device, an audible utterance of a trigger word. The method also includes, responsive to the detection of the audible utterance of the trigger word, recording audio via the smart device. The method also includes processing, via the smart device, the recorded audio to determine whether the recorded audio contains a command for the smart device or a different smart device to perform an action. The method also includes, responsive to determining that the recorded audio includes a command for the smart device or a different smart device to perform the action, determining whether the command is serviceable by the smart device without involvement of the different smart device. The method also includes, responsive to determining whether the command is serviceable by the smart device without involvement of the different smart device, taking action regarding the command.

Abstract: An audio processing system (100) accepts an audio bitstream having one of a plurality of predefined audio frame rates. The system comprises a front-end component (110), which receives a variable number of quantized spectral components, corresponding to one audio frame in any of the predefined audio frame rates, and performs an inverse quantization according to predetermined, frequency-dependent quantization levels. The front-end component may be agnostic of the audio frame rate. The audio processing system further comprises a frequency-domain processing stage (120) and a sample rate converter (130), which provide a reconstructed audio signal sampled at a target sampling frequency independent of the audio frame rate. By its frame-rate adaptability, the system can be configured to operate frame-synchronously in parallel with a video processing system that accepts plural video frame rates.

Abstract: A downscaled version of an audio decoding procedure may more effectively and/or at improved compliance maintenance be achieved if the synthesis window used for downscaled audio decoding is a downsampled version of a reference synthesis window involved in the non-downscaled audio decoding procedure by downsampling by the downsampling factor by which the downsampled sampling rate and the original sampling rate deviate, and downsampled using a segmental interpolation in segments of ¼ of the frame length.

Abstract: Techniques for lip-reading session triggering events are described. A computing device is equipped with lip-reading capability that enables the device to “read the lips” (i.e., facial features) of a user. The computing device determines when a triggering event occurs to automatically cause the computing device to switch from one input type to a lip-reading session. Lip-reading is also used in conjunction with other types of inputs to improve accuracy of the input. Machine learning is used to personalize the lip-reading capability of the computing device for a particular user.

Abstract: A software agent, that is used to assist in providing a service, receives communications from a set of users that are attempting to use the software agent. The communications include communications that are interacting with the software agent, and communications that are not interacting with the software agent. The software agent performs natural language processing on all communications to identify such things as user sentiment, user concerns or other items in the content of the messages, and also to identify actions taken by the users in order to obtain a measure of user satisfaction with the software agent. One or more action signals are then generated based upon the identified user satisfaction with the software agent.

Abstract: Methods and systems for providing message playback using a shared electronic device is described herein. In response to receiving a request to output messages, a speech-processing system may determine a group account associated with a requesting device, and may determine messages stored by a message data store for the group account. Speaker identification processing may also be performed to determine a speaker of the request. A user account associated with the speaker, and messages stored for the user account, may be determined. A summary response indicating the user account's messages and the group account's message may then be generated such that the user account messages are identified prior to the group account's messages. The messages may then be analyzed to determine an appropriate voice user interface for the requester such that the playback of the messages using a shared electronic device is more natural and conversational.

Abstract: A speech-capture device can capture audio data during wakeword monitoring and use the audio data to determine if a user is present nearby the device, even if no wakeword is spoken. Audio such as speech, human originating sounds (e.g., coughing, sneezing), or other human related noises (e.g., footsteps, doors closing) can be used to detect audio. Audio frames are individually scored as to whether a human presence is detected in the particular audio frames. The scores are then smoothed relative to nearby frames to create a decision for a particular frame. Presence information can then be sent according to a periodic schedule to a remote device to create a presence “heartbeat” that regularly identifies whether a user is detected proximate to a speech-capture device.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified task based on transcribed caller utterance. Processor samples audio caller utterance to generate samples of audio caller utterance. Processor generates loudness result based on loudness values of samples using loudness neural network associated with identified task. Processor generates pitch result based on pitch values of samples using pitch neural network associated with identified task. Processor generates tone result for each word in transcribed caller utterance using tone neural network associated with identified task. Using task completion probability neural network associated with identified task, processor generates task completion probability result that is based on at least one of: loudness result, pitch result, or tone result. Other embodiments are disclosed herein.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag.

Abstract: A call captioning system for captioning a hearing user's (HU's) voice signal during an ongoing call with an assisted user (AU) includes: an AU communication device with a display screen and a caption service activation feature, and a first processor programmed to, during an ongoing call, receive the HU's voice signal. Prior to activating the caption service via the activation feature, the processor uses an automated speech recognition (ASR) engine to generate HU voice signal captions, detect errors in the HU voice signal captions, use the errors to train the ASR software to the HU's voice signal to increase accuracy of the HU captions generated by the ASR engine; and store the trained ASR engine for subsequent use. Upon activating the caption service during the ongoing call, the processor uses the trained ASR engine to generate HU voice signal captions and present them to the AU via the display screen.

Abstract: Systems and methods of the invention determine evasiveness of postings and manage chat sessions accordingly. In embodiments, a method includes accessing a real-time text-based discourse session comprised of multiple text-based posts published by participants, the posts including a question from an author and responses from at least one respondent; determining relationships between words in the text-based discourse session utilizing corpus linguistics analysis; determining a frequency of the responses of the at least one respondent over time; determining an evasiveness score for each of the responses based on natural language processing of the responses, wherein each of the evasiveness scores indicate a level of relevance of a response with respect to the question; determining rankings for each of the responses based on the determined relationships of words, the frequency of the responses, and the evasiveness scores; and determining a display order for the responses based on the rankings of the responses.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag.

Abstract: A device may be configured to parse a syntax element specifying the number of available languages within a presentation associated with an audio stream. A device may be configured to parse one or more syntax elements identifying each of the available languages and parse an accessibility syntax element for each language within the presentation.

Abstract: An information processing apparatus, an information processing system, and an information processing method. The information processing apparatus identifies a work target and work content of work, based on voice data sent from a terminal for inputting utterance about the work by a worker, updates work implementation status information indicating work implementation status of the work stored in a memory based on the work target and work content of the work that are identified, and controls to display the work implementation status of the work based on the work implementation status information on a display terminal connected through a network.

Abstract: Methods and systems for rendering lists of instructions and performing actions associated with those lists are described herein. In some embodiments, an individual may request that a voice activated electronic device associated with their user account assist in performing a task using a list of instructions. The list of instructions may include metadata that indicates actions capable of being performed by additional Internet of Things (“IoT”) devices. When the instructions are rendered, an instructions speechlet may recognize the metadata and may cause one or more of the IoT devices to perform a particular action. Furthermore, the metadata may also correspond to content capable of being rendered by the voice activated electronic device to assist the individual in performing a particular step of the instructions.

Abstract: In some embodiments, a pitch filter for filtering a preliminary audio signal generated from an audio bitstream is disclosed. The pitch filter has an operating mode selected from one of either: (i) an active mode where the preliminary audio signal is filtered using filtering information to obtain a filtered audio signal, and (ii) an inactive mode where the pitch filter is disabled. The preliminary audio signal is generated in an audio encoder or audio decoder having a coding mode selected from at least two distinct coding modes, and the pitch filter is capable of being selectively operated in either the active mode or the inactive mode while operating in the coding mode based on control information.

Abstract: A method and device for automatically increasing the spectral bandwidth of an audio signal including generating a “mapping” (or “prediction”) matrix based on the analysis of a reference wideband signal and a reference narrowband signal, the mapping matrix being a transformation matrix to predict high frequency energy from a low frequency energy envelope, generating an energy envelope analysis of an input narrowband audio signal, generating a resynthesized noise signal by processing a random noise signal with the mapping matrix and the envelope analysis, high-pass filtering the resynthesized noise signal, and summing the high-pass filtered resynthesized noise signal with the input narrowband audio signal. Other embodiments are disclosed.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag.

Abstract: This disclosure describes techniques for identifying a voice-enabled device from a group of voice-enabled devices to respond to a speech utterance of a user. A speech-processing system may receive an audio signal representing the speech utterance captured in an environment of a voice-enabled device, and identify another voice-enabled device located in the environment. The system may analyze the audio signal using a different natural-language-understanding model for each of the voice-enabled devices to identify an intent for each of the voice-enabled devices to respond to the speech utterance. The system may determine confidence scores that the intents are responsive to the speech utterance, and select the intent with the highest confidence score. The system may use the selected intent to generate a command for the corresponding voice-enabled device to respond to the user.

Abstract: Methods, systems, and computer program product for automatically performing sentiment analysis on texts, such as telephone call transcripts and electronic written communications. Disclosed techniques include, inter alia, lexicon training, handling of negations and shifters, pruning of lexicons, confidence calculation for token orientation, supervised customization, lexicon mixing, and adaptive segmentation.