Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models

no code implementations • 27 Feb 2024 • Rohit Prabhavalkar, Zhong Meng, Weiran Wang, Adam Stooke, Xingyu Cai, Yanzhang He, Arun Narayanan, Dongseong Hwang, Tara N. Sainath, Pedro J. Moreno

In the present work, we study one such strategy: applying multiple frame reduction layers in the encoder to compress encoder outputs into a small number of output frames.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Large-scale Language Model Rescoring on Long-form Data

no code implementations • 13 Jun 2023 • Tongzhou Chen, Cyril Allauzen, Yinghui Huang, Daniel Park, David Rybach, W. Ronny Huang, Rodrigo Cabrera, Kartik Audhkhasi, Bhuvana Ramabhadran, Pedro J. Moreno, Michael Riley

In this work, we study the impact of Large-scale Language Models (LLM) on Automated Speech Recognition (ASR) of YouTube videos, which we use as a source for long-form ASR.

Language Modelling speech-recognition +1

Modular Hybrid Autoregressive Transducer

no code implementations • 31 Oct 2022 • Zhong Meng, Tongzhou Chen, Rohit Prabhavalkar, Yu Zhang, Gary Wang, Kartik Audhkhasi, Jesse Emond, Trevor Strohman, Bhuvana Ramabhadran, W. Ronny Huang, Ehsan Variani, Yinghui Huang, Pedro J. Moreno

In this work, we propose a modular hybrid autoregressive transducer (MHAT) that has structurally separated label and blank decoders to predict label and blank distributions, respectively, along with a shared acoustic encoder.

Language Modelling speech-recognition +1

G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR

no code implementations • 19 Oct 2022 • Gary Wang, Ekin D. Cubuk, Andrew Rosenberg, Shuyang Cheng, Ron J. Weiss, Bhuvana Ramabhadran, Pedro J. Moreno, Quoc V. Le, Daniel S. Park

Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition

no code implementations • 13 Sep 2022 • Kartik Audhkhasi, Yinghui Huang, Bhuvana Ramabhadran, Pedro J. Moreno

We investigate a few approaches to increasing attention head diversity, including using different attention mechanisms for each head and auxiliary training loss functions to promote head diversity.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

A Scalable Model Specialization Framework for Training and Inference using Submodels and its Application to Speech Model Personalization

no code implementations • 23 Mar 2022 • Fadi Biadsy, Youzheng Chen, Xia Zhang, Oleg Rybakov, Andrew Rosenberg, Pedro J. Moreno

We also show that learning a speaker-embedding space can scale further and reduce the amount of personalization training data required per speaker.