Hot word free adaptation, of one or more function(s) of an automated assistant, responsive to determining, based on gaze measure(s) and/or active speech measure(s), that a user is engaging with the automated assistant. Implementations relate to various techniques for mitigating false positive occurrences of and/or false negative occurrences, of hot word free adaptation, through utilization of personalized parameter(s) for at least some user(s) of an assistant device. The personalized parameter(s) are utilized in determining whether condition(s) are satisfied, where those condition(s), if satisfied, indicate that the user is engaging in hot word free interaction with the automated assistant and result in adaptation of function(s) of the automated assistant.

There is provided a computer implemented method of aligning an automatically generated transcription of an audio recording to a manually generated transcription of the audio recording comprising: identifying non-aligned text fragments, each located between respective two non-continuous aligned text-fragments of the automatically generated transcription, each aligned text-fragment matching words of the manually generated transcription, for each respective non-aligned text fragment: mapping a target keyword of the manually generated transcription to phonemes, mapping the respective non-aligned text fragment to a corresponding audio-fragment of the audio recording, mapping the audio-fragment to phonemes, identifying at least some of the phonemes of the audio-fragment that correspond to the phonemes of the target keyword, and mapping the identified at least some of the phonemes of the audio-fragment to a corresponding word of the automatically generated transcript, wherein the corresponding word is an incorrect automated transcription of the target word appearing in the manually generated transcription.

There is provided a computer implemented method of aligning an automatically generated transcription of an audio recording to a manually generated transcription of the audio recording comprising: identifying non-aligned text fragments, each located between respective two non-continuous aligned text-fragments of the automatically generated transcription, each aligned text-fragment matching words of the manually generated transcription, for each respective non-aligned text fragment: mapping a target keyword of the manually generated transcription to phonemes, mapping the respective non-aligned text fragment to a corresponding audio-fragment of the audio recording, mapping the audio-fragment to phonemes, identifying at least some of the phonemes of the audio-fragment that correspond to the phonemes of the target keyword, and mapping the identified at least some of the phonemes of the audio-fragment to a corresponding word of the automatically generated transcript, wherein the corresponding word is an incorrect automated transcription of the target word appearing in the manually generated transcription.

Systems and methods described herein relate to adapting a language model for automatic speech recognition (ASR) for a new set of words. Instead of retraining the ASR models, language models and grammar models, the system only modifies one grammar model and ensures its compatibility with the existing models in the ASR system.

The disclosure relates to an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and an application thereof. In particular, an electronic apparatus, a document displaying method thereof, and a non-transitory computer readable recording medium are provided. An electronic apparatus according to an embodiment of the disclosure includes a display unit displaying a document, a microphone receiving a user voice, and a processor configured to acquire at least one topic from contents included in a plurality of pages constituting the document, recognize a voice input through the microphone, match the recognized voice with one of the acquired at least one topic, and control the display unit to display a page including the matched topic.

Utilizing an adaptive multichannel technique to mitigate reverberation present in received audio signals, prior to providing corresponding audio data to one or more additional component(s), such as automatic speech recognition (ASR) components. Implementations disclosed herein are “adaptive”, in that they utilize a filter, in the reverberation mitigation, that is online, causal and varies depending on characteristics of the input. Implementations disclosed herein are “multichannel”, in that a corresponding audio signal is received from each of multiple audio transducers (also referred to herein as “microphones”) of a client device, and the multiple audio signals (e.g., frequency domain representations thereof) are utilized in updating of the filter—and dereverberation occurs for audio data corresponding to each of the audio signals (e.g., frequency domain representations thereof) prior to the audio data being provided to ASR component(s) and/or other component(s).

Utilizing an adaptive multichannel technique to mitigate reverberation present in received audio signals, prior to providing corresponding audio data to one or more additional component(s), such as automatic speech recognition (ASR) components. Implementations disclosed herein are “adaptive”, in that they utilize a filter, in the reverberation mitigation, that is online, causal and varies depending on characteristics of the input. Implementations disclosed herein are “multichannel”, in that a corresponding audio signal is received from each of multiple audio transducers (also referred to herein as “microphones”) of a client device, and the multiple audio signals (e.g., frequency domain representations thereof) are utilized in updating of the filter—and dereverberation occurs for audio data corresponding to each of the audio signals (e.g., frequency domain representations thereof) prior to the audio data being provided to ASR component(s) and/or other component(s).

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting an end of a query are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance spoken by a user. The actions further include applying, to the audio data, an end of query model. The actions further include determining the confidence score that reflects a likelihood that the utterance is a complete utterance. The actions further include comparing the confidence score that reflects the likelihood that the utterance is a complete utterance to a confidence score threshold. The actions further include determining whether the utterance is likely complete or likely incomplete. The actions further include providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance in an active state or (ii) deactivate the microphone that is receiving the utterance.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting an end of a query are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance spoken by a user. The actions further include applying, to the audio data, an end of query model. The actions further include determining the confidence score that reflects a likelihood that the utterance is a complete utterance. The actions further include comparing the confidence score that reflects the likelihood that the utterance is a complete utterance to a confidence score threshold. The actions further include determining whether the utterance is likely complete or likely incomplete. The actions further include providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance in an active state or (ii) deactivate the microphone that is receiving the utterance.

In one embodiment, a method includes generating audio segments from a speech signal, generating latent representations that respectively correspond to the audio segments, the latent representations comprising a first subset and a second subset, generating quantized representations that respectively correspond to the latent representations, masking the second subset of the latent representations, using a machine-learning model to process the first subset of the latent representations and the masked second subset of the latent representations to generate contextualized representations that respectively correspond to the latent representations, pre-training the machine-learning model based on comparisons between (1) a subset of the contextualized representations that respectively correspond to the masked second subset of the latent representations and (2) a subset of the quantized representations that respectively correspond to the masked second subset of the latent representations, and training the pre-trained machine-learning model to perform a speech analysis task.