Google Inc.

Françoise Beaufays

Pedro Jose Moreno Mengibar

Johan Schalkwyk

Fuchun Peng

Khe Chai Sim

Rajiv Mathews

Andrew Hard

Dragan Zivkovic

Kurt Edward Partridge

Giovanni Motta

Trevor D Strohman

Om Dipakbhai Thakkar

Rohit Prakash Prabhavalkar

Xin Lei

Swaroop Indra Ramaswamy

Akash Agrawal

Petr Zadrazil

Oren Litvin

Lillian Zhou

Tamar Lucassen

Mingqing Chen

Dhruv Guliani

Harry Zhang

Florian Tramèr

Brian Patrick Strope

Techniques are disclosed that enable generating jointly probable output by processing input using a multi-stream recurrent neural network transducer (MS RNN-T) model. Various implementations include generating a first output sequence and a second output sequence by processing a single input sequence using the MS RNN-T, where the first output sequence is jointly probable with the second output sequence. Additional or alternative techniques are disclosed that enable generating output by processing multiple input sequences using the MS RNN-T. Various implementations include processing a first input sequence and a second input sequence using the MS RNN-T to generate output. In some implementations, the MS RNN-T can be used to process two or more input sequences to generate two or more jointly probable output sequences.

Multi-stream recurrent neural network transducer(s)

Implementations disclosed herein are directed to systems and methods for evaluating on-device machine learning (ML) model(s) based on performance measure(s) of client device(s) and/or the on-device ML model(s). The client device(s) can include on-device memory that stores the on-device ML model(s) and a plurality of testing instances for the on-device ML model(s). When certain condition(s) are satisfied, the client device(s) can process, using the on-device ML model(s), the plurality of testing instances to generate the performance measure(s). The performance measure(s) can include, for example, latency measure(s), memory consumption measure(s), CPU usage measure(s), ML model measure(s) (e.g., precision and/or recall), and/or other measures. In some implementations, the on-device ML model(s) can be activated (or kept active) for use locally at the client device(s) based on the performance measure(s). In other implementations, the on-device ML model(s) can be sparsified based on the performance measure(s).

Evaluating on-device machine learning model(s) based on performance measures of client device(s) and/or the on-device machine learning model(s)

Processor(s) of a client device can: identify a textual segment stored locally at the client device; process the textual segment, using a speech synthesis model stored locally at the client device, to generate synthesized speech audio data that includes synthesized speech of the identified textual segment; process the synthesized speech, using an on-device speech recognition model that is stored locally at the client device, to generate predicted output; and generate a gradient based on comparing the predicted output to ground truth output that corresponds to the textual segment. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

On-device speech synthesis of textual segments for training of on-device speech recognition model

Implementations disclosed herein are directed to unsupervised federated training of global machine learning ('ML') model layers that, after the federated training, can be combined with additional layer(s), thereby resulting in a combined ML model. Processor(s) can: detect audio data that captures a spoken utterance of a user of a client device; process, using a local ML model, the audio data to generate predicted output(s); generate, using unsupervised learning locally at the client device, a gradient based on the predicted output(s); transmit the gradient to a remote system; update weight(s) of the global ML model layers based on the gradient; subsequent to updating the weight(s), train, using supervised learning remotely at the remote system, a combined ML model that includes the updated global ML model layers and additional layer(s); transmit the combined ML model to the client device; and use the combined ML model to make prediction(s) at the client device.

Unsupervised federated learning of machine learning model layers

Implementations described herein identify and correct automatic speech recognition (ASR) misrecognitions. For example, on-device processor(s) of a client device may generate a predicted textual segment that is predicted to correspond to spoken utterance of a user of the client device, and may receive further input that modifies the predicted textual segment to an alternate textual segment. Further, the on-device processor(s) may store these textual segments in on-device storage as a candidate correction pair, and transmit the candidate correction pair to a remote system. Moreover, remote processor(s) of the remote system may determine that the candidate correction pair is an actual correction pair, and may cause client devices to generate updates for a global ASR model for the candidate correction pair. Additionally, the remote processor(s) may distribute the global ASR model to the client devices and/or additional client devices.

Identifying and correcting automatic speech recognition (ASR) misrecognitions in a decentralized manner

Processor(s) of a client device can: receive sensor data that captures environmental attributes of an environment of the client device; process the sensor data using a machine learning model to generate a predicted output that dictates whether one or more currently dormant automated assistant functions are activated; making a decision as to whether to trigger the one or more currently dormant automated assistant functions; subsequent to making the decision, determining that the decision was incorrect; and in response to determining that the determination was incorrect, generating a gradient based on comparing the predicted output to ground truth output. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

Using corrections, of automated assistant functions, for training of on-device machine learning models

Implementations disclosed herein are directed to federated learning of machine learning ('ML') model(s) based on gradient(s) generated at corresponding client devices and a remote system. Processor(s) of the corresponding client devices can process client data generated locally at the corresponding client devices using corresponding on-device ML model(s) to generate corresponding predicted outputs, generate corresponding client gradients based on the corresponding predicted outputs, and transmit the corresponding client gradients to the remote system. Processor(s) of the remote system can process remote data obtained from remote database(s) using global ML model(s) to generate additional corresponding predicted outputs, generate corresponding remote gradients based on the additional corresponding predicted outputs. Further, the remote system can utilize the corresponding client gradients and the corresponding remote gradients to update the global ML model(s) or weights thereof. The updated global ML model(s) and/or the updated weights thereof can be transmitted back to the corresponding client devices.

Mixed client-server federated learning of machine learning model(s)

Processor(s) of a client device can: receive audio data that captures a spoken utterance of a user of the client device; process, using an on-device speech recognition model, the audio data to generate a predicted textual segment that is a prediction of the spoken utterance; cause at least part of the predicted textual segment to be rendered (e.g., visually and/or audibly); receive further user interface input that is a correction of the predicted textual segment to an alternate textual segment; and generate a gradient based on comparing at least part of the predicted output to ground truth output that corresponds to the alternate textual segment. The gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model and/or is transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

Using corrections, of predicted textual segments of spoken utterances, for training of on-device speech recognition model

Implementations disclosed herein are directed to ephemeral learning of machine learning ('ML') model(s) based on gradient(s) generated at a remote system (e.g., remote server(s)). Processor(s) of the remote system can receive stream(s) of audio data capturing spoken utterance(s) from a client device of a user. A fulfillment pipeline can process the stream(s) of audio data to cause certain fulfillment(s) of the spoken utterance(s) to be performed. Meanwhile, a training pipeline can process the stream(s) of audio data to generate gradient(s) using unsupervised learning techniques. Subsequent to the processing by the fulfillment pipeline and/or the training pipeline, the stream(s) of audio data are discarded by the remote system. Accordingly, the ML model(s) can be trained at the remote system without storing or logging of the stream(s) of audio data by non-transient memory thereof, thereby providing more efficient training mechanisms for training the ML model(s) and also increasing security of user data.

Growing community of inventors

Mountain View, CA, United States of America

Françoise Beaufays