A speech recognition apparatus 20, includes; a data acquisition unit 21 that acquires speech data and sensor data to be recognized; a speech recognition unit 22 that converts the acquired speech data into text data by applying the acquired speech data and the acquired sensor data to an acoustic model which is constructed by machine learning using an embedded vector generated from sensor data related to training data in addition to speech data to be the training data and teacher data to be the training data.

A speech recognition apparatus 20, includes; a data acquisition unit 21 that acquires speech data and sensor data to be recognized; a speech recognition unit 22 that converts the acquired speech data into text data by applying the acquired speech data and the acquired sensor data to an acoustic model which is constructed by machine learning using an embedded vector generated from sensor data related to training data in addition to speech data to be the training data and teacher data to be the training data.

An information processing method includes receiving first text information, which is generated according to a speech, input through a first input device; receiving audio information recorded by a second input device, wherein the audio information is generated and recorded according to the speech; performing speech recognition on the audio information to obtain second text information; and presenting the first text information and the second text information. A correspondence relationship exists between content in the first text information and content in the second text information.

A smart device input method based on facial vibration includes: collecting a facial vibration signal generated when a user performs voice input; extracting a Mel-frequency cepstral coefficient from the facial vibration signal; and taking the Mel-frequency cepstral coefficient as an observation sequence to obtain text input corresponding to the facial vibration signal by using a trained hidden Markov model. The facial vibration signal is collected by a vibration sensor arranged on glasses. The vibration signal is processed by: amplifying the collected facial vibration signal; transmitting the amplified facial vibration signal to the smart device via a wireless module; and intercepting a section from the received facial vibration signal as an effective portion and extracting the Mel-frequency cepstral coefficient from the effective portion by the smart device.

A smart device input method based on facial vibration includes: collecting a facial vibration signal generated when a user performs voice input; extracting a Mel-frequency cepstral coefficient from the facial vibration signal; and taking the Mel-frequency cepstral coefficient as an observation sequence to obtain text input corresponding to the facial vibration signal by using a trained hidden Markov model. The facial vibration signal is collected by a vibration sensor arranged on glasses. The vibration signal is processed by: amplifying the collected facial vibration signal; transmitting the amplified facial vibration signal to the smart device via a wireless module; and intercepting a section from the received facial vibration signal as an effective portion and extracting the Mel-frequency cepstral coefficient from the effective portion by the smart device.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating language model biasing. In some implementations, context data is received. A likely context associated with a user is determined based on at least a portion of the context data. One or more language model biasing parameters based at least on the likely context associated with the user is selected. A context confidence score associated with the likely context based on at least a portion of the context data is determined. One or more language model biasing parameters based at least on the context confidence score is adjusted. A baseline language model based at least on the one or more of the adjusted language model biasing parameters is biased. The baseline language model is provided for use by an automated speech recognizer (ASR).

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating language model biasing. In some implementations, context data is received. A likely context associated with a user is determined based on at least a portion of the context data. One or more language model biasing parameters based at least on the likely context associated with the user is selected. A context confidence score associated with the likely context based on at least a portion of the context data is determined. One or more language model biasing parameters based at least on the context confidence score is adjusted. A baseline language model based at least on the one or more of the adjusted language model biasing parameters is biased. The baseline language model is provided for use by an automated speech recognizer (ASR).

The present teaching relates to method, system, medium, and implementations for enabling communication with a user. Information representing surrounding of a user engaged in an on-going dialogue is received via the communication platform, wherein the information includes a current response from the user in the on-going dialogue and is acquired from a current scene in which the user is present and captures characteristics of the user and the current scene. Relevant features are extracted from the information. A state of the user is estimated based on the relevant features and a dialogue context surrounding the current scene is determined based on the relevant features. A feedback directed to the current response of the user is generated based on the state of the user and the dialogue context.

The present teaching relates to method, system, medium, and implementations for enabling communication with a user. Information representing surrounding of a user engaged in an on-going dialogue is received via the communication platform, wherein the information includes a current response from the user in the on-going dialogue and is acquired from a current scene in which the user is present and captures characteristics of the user and the current scene. Relevant features are extracted from the information. A state of the user is estimated based on the relevant features and a dialogue context surrounding the current scene is determined based on the relevant features. A feedback directed to the current response of the user is generated based on the state of the user and the dialogue context.

Systems and methods are described for processing and interpreting audible commands spoken in one or more languages. Speech recognition systems disclosed herein may be used as a stand-alone speech recognition system or comprise a portion of another content consumption system. A requesting user may provide audio input (e.g., command data) to the speech recognition system via a computing device to request an entertainment system to perform one or more operational commands. The speech recognition system may analyze the audio input across a variety of linguistic models, and may parse the audio input to identify a plurality of phrases and corresponding action classifiers. In some embodiments, the speech recognition system may utilize the action classifiers and other information to determine the one or more identified phrases that appropriately match the desired intent and operational command associated with the user's spoken command.