Abstract: A soft decision audio decoding system for preserving audio continuity in a digital wireless audio receiver is provided that deduces the likelihood of errors in a received digital signal, based on generated hard bits and soft bits. The soft bits may be utilized by a soft audio decoder to determine whether the digital signal should be decoded or muted. The soft bits may be generated based on the detected point and a detected noise power, or by using a soft-output Viterbi algorithm. The value of the soft bits may indicate confidence in the strength of the hard bit generation. The soft decision audio decoding system may infer errors and decode perceptually acceptable audio without requiring error detection, as in conventional systems, as well as have low latency and improved granularity.

Abstract: Techniques are provided for reducing the latency of automatic speech recognition using hypothesis score trend analysis. A methodology implementing the techniques according to an embodiment includes generating complete-phrase hypotheses and partial-phrase hypotheses, along with associated likelihood scores, based on a segment of speech. The method also includes selecting the complete-phrase hypothesis associated with the highest of the complete-phrase hypotheses likelihood scores, and selecting the partial-phrase hypothesis associated with the highest of the partial-phrase hypotheses likelihood scores. The method further includes calculating a relative likelihood score based on a ratio of the likelihood score associated with the selected complete-phrase hypothesis to the likelihood score associated with the selected partial-phrase hypothesis.

Abstract: A method, a computer program product, and a computer system for mapping between a speech signal and a transcript of the speech signal. The computer system segments the speech signal to obtain one or more segmented speech signals and the transcript of the speech signal to obtain one or more segmented transcripts of the speech signal. The computer system generates estimated phone sequences and reference phone sequences, calculates costs of correspondences between the estimated phone sequences and the reference phone sequences, determines a series of the estimated phone sequences with a smallest cost, selects a partial series of the estimated phone sequences from the series of the estimated phone sequences, and generates mapping data which includes the partial series of the estimated phone sequences and a corresponding series of the reference phone sequences.

Abstract: Systems and method of diarization of audio files use an acoustic voiceprint model. A plurality of audio files are analyzed to arrive at an acoustic voiceprint model associated to an identified speaker. Metadata associate with an audio file is used to select an acoustic voiceprint model. The selected acoustic voiceprint model is applied in a diarization to identify audio data of the identified speaker.

Abstract: A method and device for automatically changing or updating a configuration or setting of a communication system is disclosed. In one aspect, the method includes providing information to the communication system, the information comprising natural human language, storing the information in a digital storage device, detecting a triggering event in the information, and changing the configuration or setting of the communication system automatically using a processor. The information is an input to the communication system, an input from at least one alternate communication system, or a combination of an input to the communication system and an input from the at least one alternate communication system.

Abstract: A signal processing device, method, and program that may obtain audio at a higher audio quality when decoding an audio signal. An envelope information generating unit generates envelope information representing an envelope form of high frequency components of an audio signal to be encoded. A sine wave information generating unit extracts a sine wave signal from the high frequency components of the audio signal, and generates a sine wave information representing an emergence start position of the sine wave signal. An encoding stream generating unit multiplexes the envelope information, the sine wave information, and low frequency components of the audio signal that have been encoded, and outputs an encoding stream obtained as the result. The high frequency components included in the sine wave signal may be predicted at a higher accuracy from the envelope information and the sine wave information at the receiving side of the encoding stream.

Abstract: Embodiments are disclosed for transliterating text entries across different script systems. A method according to some embodiments includes steps of: receiving an input string in a first script system input using a keyboard; segmenting, using a probabilistic model, the input string into phonemes that correspond to characters or sets of characters in a second script system; converting the phonemes in the first script system into the characters or sets of characters in the second script system, the characters or sets of characters forming a word or a word prefix in the second script system; and outputting the word or the word prefix in the second script system.

Abstract: An approach is provided in which a fault-injecting system injects a natural language fault into a first text segment to produce a second text segment that are both written in a natural language. The fault-injecting system receives a third text segment from a reviewer that includes at least one correction to the second text segment. The fault-injecting system compares the third text segment against the first text segment and generates an efficacy score. The efficacy score indicates whether the correction in the third text segment corrects the natural language fault. In turn, the fault-injecting system sends the efficacy score to an author of the first text segment.

Abstract: Systems and methods diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing speech recognition by generating a neural network output from an audio data input sequence, where the neural network output characterizes words spoken in the audio data input sequence. One of the methods includes, for each of the audio data inputs, providing a current audio data input sequence that comprises the audio data input and the audio data inputs preceding the audio data input in the audio data input sequence to a convolutional subnetwork comprising a plurality of dilated convolutional neural network layers, wherein the convolutional subnetwork is configured to, for each of the plurality of audio data inputs: receive the current audio data input sequence for the audio data input, and process the current audio data input sequence to generate an alternative representation for the audio data input.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboration between multiple voice controlled devices are disclosed. In one aspect, a method includes the actions of identifying, by a first computing device, a second computing device that is configured to respond to a particular, predefined hotword; receiving audio data that corresponds to an utterance; receiving a transcription of additional audio data outputted by the second computing device in response to the utterance; based on the transcription of the additional audio data and based on the utterance, generating a transcription that corresponds to a response to the additional audio data; and providing, for output, the transcription that corresponds to the response.

Abstract: In accordance with an example embodiment of the present invention, disclosed is a method and an apparatus thereof for controlling a concealment method for a lost audio frame of a received audio signal. A method for a decoder of concealing a lost audio frame comprises detecting in a property of the previously received and reconstructed audio signal, or in a statistical property of observed frame losses, a condition for which the substitution of a lost frame provides relatively reduced quality. In case such a condition is detected, the concealment method is modified by selectively adjusting a phase or a spectrum magnitude of a substitution frame spectrum.

Abstract: A domestic appliance includes a user interface for a user to input commands, a camera for taking an image of an operating area from which the user interface can be operated by the user, a speech recognition device for detecting a speech command, and a control device configured to determine a level of security depending on the image that was taken by the camera and to execute the speech command detected by the speech recognition device depending on the level of security that has been determined.

Abstract: Methods and apparatus for identifying a music service based on a user command. A content type is identified from a received user command and a music service is selected that supports the content type. A selected music service can then transmit audio content associated with the content type for playback.

Abstract: Aspects relate to computer implemented methods and systems for monitoring a user's hearing and comprehension. The methods and systems include receiving, by an audio capture device, a first audio input, receiving, by the audio capture device, a second audio input, converting the first and second audio inputs into respective first and second audio signals, transcribing the first and second audio signals into respective first and second transcriptions, analyzing, by a processor of the remote resource, the first and second transcriptions to determine if a content of the second transcription is related to a content of the first transcription to determine degradation of hearing of the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling screen-specific user interfacing with elements of viewable screens presented by an electronic device are disclosed. In one aspect, a method includes the actions of identifying a character sequence representing a first input that is received while displaying a viewable screen having at least one selectable viewable element. The actions further include accessing an electronic file that provides a text representation of one or more of the at least one selectable viewable element. The actions further include comparing the character sequence to the text representation. The actions further include selecting, within the viewable screen, a selectable viewable element whose text representation matches the character sequence. The actions further include triggering any action linked to the selecting the selectable viewable element.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboration between multiple voice controlled devices are disclosed. In one aspect, a method includes the actions of identifying, by a first computing device, a second computing device that is configured to respond to a particular, predefined hotword; receiving audio data that corresponds to an utterance; receiving a transcription of additional audio data outputted by the second computing device in response to the utterance; based on the transcription of the additional audio data and based on the utterance, generating a transcription that corresponds to a response to the additional audio data; and providing, for output, the transcription that corresponds to the response.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: An audio signal synthesizer generates a synthesis audio signal having a first frequency band and a second synthesized frequency band derived from the first frequency band and comprises a patch generator, a spectral converter, a raw signal processor and a combiner. The patch generator performs at least two different patching algorithms, each patching algorithm generating a raw signal. The patch generator is adapted to select one of the at least two different patching algorithms in response to a control information. The spectral converter converts the raw signal into a raw signal spectral representation. The raw signal processor processes the raw signal spectral representation in response to spectral domain spectral band replication parameters to obtain an adjusted raw signal spectral representation.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.