Google Inc.

Capital One Services, LLC

Neeraj Gaur

Pedro Jose Moreno Mengibar

Parisa Haghani

Rohit Prakash Prabhavalkar

Yaniv Leviathan

Arun Narayanan

Tomer Amiaz

Asaf Aharoni

Austin Severn Waters

Zhongdi Qu

Galen Tsai Chuang

Nir Shabat

Bhuvana Ramabhadran

Michiel A U Bacchiani

Ehsan Variani

Tongzhou Chen

Brian Farris

Rashan Jibowu

Tara N Sainath

Trevor D Strohman

David Rybach

Weiran Wang

Yangzhang He

Ronny Huang

Charles Caleb Peyser

Sepand Mavandadi

Michiel Au Bacchiani

Isabel Leal

Manasa Prasad

Yun Zhu

A method for distilling one or more trained teacher automatic speech recognition (ASR) models into a multilingual student model includes receiving a plurality of teacher training examples and a plurality of student training examples. The method also includes training one or more teacher automatic speech recognition (ASR) models using the plurality of teacher training examples. Each teacher ASR model is configured to output a respective textual representation of a respective audio input. The method further includes generating a multi-lingual student ASR model by training the multi-lingual student ASR model using the plurality of student training examples and distilling the trained one or more teacher ASR models into the multilingual student ASR model using a tunable distillation loss weight. Each student ASR model is configured to receive an audio input and output a corresponding textual representation of the received audio input.

Self-adaptive distillation

A method includes generating, using an audio encoder, a higher-order feature representation for each acoustic frame in a sequence of acoustic frames; generating, using a decoder, based on the higher-order feature representation, a plurality of speech recognition hypotheses, each hypotheses corresponding to a candidate transcription of an utterance and having an associated first likelihood score; generating, using an external language model, for each speech recognition hypothesis, a second likelihood score; determining, using a learnable fusion module, for each speech recognition hypothesis, a set of fusion weights based on the higher-order feature representation and the speech recognition hypothesis; and generating, using the learnable fusion module, for each speech recognition hypothesis, a third likelihood score based on the first likelihood score, the second likelihood score, and the set of fusion weights, the audio encoder and decoder trained using minimum additive error rate training in the presence of the external language model.

Rare word recognition with LM-aware MWER training

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.

Automated calling system

A method includes receiving a sequence of acoustic frames extracted from audio data corresponding to an utterance. During a first pass, the method includes processing the sequence of acoustic frames to generate N candidate hypotheses for the utterance. During a second pass, and for each candidate hypothesis, the method includes: generating a respective un-normalized likelihood score; generating a respective external language model score; generating a standalone score that models prior statistics of the corresponding candidate hypothesis; and generating a respective overall score for the candidate hypothesis based on the un-normalized likelihood score, the external language model score, and the standalone score. The method also includes selecting the candidate hypothesis having the highest respective overall score from among the N candidate hypotheses as a final transcription of the utterance.

Multilingual re-scoring models for automatic speech recognition

A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method for providing customized financial advice. The system may receive transaction data for a transaction associated with a customer and satisfaction data associated with the transaction. Based on the received transaction data and satisfaction data, the system may update a financial state of the customer and a financial policy for determining one or more actions to take in order to maximize a cumulative reward associated with the customer. The system may determine and output a recommended action based on the updated financial policy and customer financial state.

Systems and methods for providing customized financial advice

Growing community of inventors

Jersey City, NJ, United States of America

Neeraj Gaur