Apparatus and methods for leveraging machine learning and artificial intelligence to generate a response to an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score. The methods may further include, based on the utterance intent, the label and the score, to output a response.

Systems and methods are disclosed for generating internal state representations of a neural network during processing and using the internal state representations for classification or search. In some embodiments, the internal state representations are generated from the output activation functions of a subset of nodes of the neural network. The internal state representations may be used for classification by training a classification model using internal state representations and corresponding classifications. The internal state representations may be used for search, by producing a search feature from an search input and comparing the search feature with one or more feature representations to find the feature representation with the highest degree of similarity.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for speech recognition. One of the methods includes receiving a base language model for speech recognition including a first word sequence having a base probability value; receiving a voice search query associated with a query context; determining that a customized language model is to be used when the query context satisfies one or more criteria associated with the customized language model; obtaining the customized language model, the customized language model including the first word sequence having an adjusted probability value being the base probability value adjusted according to the query context; and converting the voice search query to a text search query based on one or more probabilities, each of the probabilities corresponding to a word sequence in a group of one or more word sequences, the group including the first word sequence having the adjusted probability value.

A method and a device for waking up via a speech based on artificial intelligence are provided in the present disclosure. The method includes: clustering phones to select garbage phones for representing the phones; constructing an alternative wake-up word approximate to a preset wake-up word according to the preset wake-up word; constructing a decoding network according to the garbage phones, the alternative wake-up word and the preset wake-up word; and waking up via the speech by using the decoding network. Due to the data size for the garbage phones is significantly smaller than the data size for the garbage words, a problem that the data size occupied is too large by using a garbage word model in the prior art is solved. Meanwhile, as a word is composed of several phones, the garbage phones may be more likely to cover all words than the garbage words. Thus, an accuracy of waking up is improved and a probability of false waking up is reduced.

An automatic speech recognition system for a vehicle includes a controller configured to select an acoustic model from a library of acoustic models based on ambient noise in a cabin of the vehicle and operating parameters of the vehicle. The controller is further configured to apply the selected acoustic model to noisy speech to improve recognition of the speech.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for speech recognition. One of the methods includes receiving a base language model for speech recognition including a first word sequence having a base probability value; receiving a voice search query associated with a query context; determining that a customized language model is to be used when the query context satisfies one or more criteria associated with the customized language model; obtaining the customized language model, the customized language model including the first word sequence having an adjusted probability value being the base probability value adjusted according to the query context; and converting the voice search query to a text search query based on one or more probabilities, each of the probabilities corresponding to a word sequence in a group of one or more word sequences, the group including the first word sequence having the adjusted probability value.

Example implementations described herein are directed to a dialog system with self-learning natural language understanding (NLU), involving a client-server configuration. If the NLU results in the client is not confident, the NLU will be done again in the server. In the dialog system, the human user and the system communicate via speech or text information. The examples of such products include robots, interactive voice response system (IVR) for call centers, voice-enabled personal devices, car navigation system, smart phones, and voice input devices in the work environments where the human operator cannot operate the devices by hands.

Generally discussed herein are devices, systems, and methods for generating a phrase that is confusing to a language classifier. A method can include determining, by the LC, a first classification score (CS) of a prompt indicating whether the prompt is a first class or a second class, predicting, based on the prompt and by a pre-trained language model (PLM), likely next words and a corresponding probability for each of the likely next words, determining, by the LC, a second CS for each of the likely next words, determining, by an adversarial classifier, respective scores for each of the likely next words, the respective scores determined based on the first CS of the prompt, the second CS of the likely next words, and the probabilities of the likely next words, and selecting, by an adversarial classifier, a next word of the likely next words based on the respective scores.

Systems and methods are disclosed for customizing a neural network for a custom dataset, when the neural network has been trained on data from a general dataset. The neural network may comprise an output layer including one or more nodes corresponding to candidate outputs. The values of the nodes in the output layer may correspond to a probability that the candidate output is the correct output for an input. The values of the nodes in the output layer may be adjusted for higher performance when the neural network is used to process data from a custom dataset.

A method and apparatus for training a recognition model and a recognition method and apparatus using the model are disclosed. The apparatus for training the model obtains an estimation hidden vector output from a hidden layer of the model in response to an estimation output vector output from the model at a previous time being input into the model at a current time, and trains the model such that the estimation hidden vector of the current time matches an answer hidden vector output from the hidden layer in response to an answer output vector, corresponding to the estimation output vector of the previous time, being input into the model at the current time.