Voice data transmission with adaptive redundancy creates a voice data packet by packetizing the voice data payload and a number of redundant payloads selected from a set of previous voice data payloads. The voice data from the voice data payload is analysed to determine whether it is a critical or non-critical payload by classifying the received voice data as voiced or unvoiced. If at least a portion of the voice data is classified as unvoiced, the voice data payload is determined to be a critical payload. If it is a critical payload, then the voice data payload is added to the set of previous voice data payloads for inclusion as a redundant payload in subsequent voice data packets. The voice data packet is then forwarded for transmission over the network.

Voice data transmission with adaptive redundancy creates a voice data packet by packetizing the voice data payload and a number of redundant payloads selected from a set of previous voice data payloads. The voice data from the voice data payload is analysed to determine whether it is a critical or non-critical payload by classifying the received voice data as voiced or unvoiced. If at least a portion of the voice data is classified as unvoiced, the voice data payload is determined to be a critical payload. If it is a critical payload, then the voice data payload is added to the set of previous voice data payloads for inclusion as a redundant payload in subsequent voice data packets. The voice data packet is then forwarded for transmission over the network.

According to some aspects, disclosed methods and systems may include having a user input one or more speech commands into an input device of a user device. The user device may communicate with one or more components or devices at a local office or headend. The local office or the user device may transcribe the speech commands into language transcriptions. The local office or the user device may determine a mood for the user based on whether any of the speech commands may have been repeated. The local office or the user device may determine, based on the mood of the user, which content asset or content service to make available to the user device.

A system includes a microphone and a computing device including a processor and a memory. The memory stores instructions executable by the processor to identify a word sequence in audio input received from the microphone, to determine a behavior pattern from the word sequence, and to report the behavior pattern to a remote server at a specified time.

A system includes a microphone and a computing device including a processor and a memory. The memory stores instructions executable by the processor to identify a word sequence in audio input received from the microphone, to determine a behavior pattern from the word sequence, and to report the behavior pattern to a remote server at a specified time.

An urgency level estimation technique of estimating an urgency level of a speaker for free uttered speech, which does not require a specific word, is provided. An urgency level estimation apparatus includes a feature amount extracting part configured to extract a feature amount of an utterance from uttered speech, and an urgency level estimating part configured to estimate an urgency level of a speaker of the uttered speech from the feature amount based on a relationship between a feature amount extracted from uttered speech and an urgency level of a speaker of the uttered speech, the relationship being determined in advance, and the feature amount includes at least one of a feature indicating speaking speed of the uttered speech, a feature indicating voice pitch of the uttered speech and a feature indicating a power level of the uttered speech.

An urgency level estimation technique of estimating an urgency level of a speaker for free uttered speech, which does not require a specific word, is provided. An urgency level estimation apparatus includes a feature amount extracting part configured to extract a feature amount of an utterance from uttered speech, and an urgency level estimating part configured to estimate an urgency level of a speaker of the uttered speech from the feature amount based on a relationship between a feature amount extracted from uttered speech and an urgency level of a speaker of the uttered speech, the relationship being determined in advance, and the feature amount includes at least one of a feature indicating speaking speed of the uttered speech, a feature indicating voice pitch of the uttered speech and a feature indicating a power level of the uttered speech.

A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

A method of extracting a fundamental frequency of an input sound includes generating a DJ transform spectrogram indicating estimated pure-tone amplitudes for respective natural frequencies of a plurality of springs and a plurality of time points by calculating the estimated pure-tone amplitudes for the respective natural frequencies by modeling an oscillation motion of the plurality of springs having different natural frequencies with respect to an input sound, calculating degrees of fundamental frequency suitability based on a moving average of the estimated pure-tone amplitudes or on a moving standard deviation of the estimated pure-tone amplitudes with respect to each natural frequency of the DJ transform spectrogram, and extracting a fundamental frequency based on local maximum values of the degrees of fundamental frequency suitability for the respective natural frequencies at each of the plurality of time points.