184 citations · 557 across the 54 of their papers we have counts for
60 papers
Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers
Adam Stooke, Rohit Prabhavalkar, Khe Chai Sim +1
Modern systems for automatic speech recognition, including the RNN-Transducer and Attention-based Encoder-Decoder (AED), are designed so that the encoder is not required to alter t…
Text Injection for Neural Contextual Biasing
Zhong Meng, Zelin Wu, Rohit Prabhavalkar +5
Neural contextual biasing effectively improves automatic speech recognition (ASR) for crucial phrases within a speaker's context, particularly those that are infrequent in the trai…
Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping
Lun Wang, Om Thakkar, Zhong Meng +3
Gradient clipping plays a vital role in training large-scale automatic speech recognition (ASR) models. It is typically applied to minibatch gradients to prevent gradient explosion…
Deferred NAM: Low-latency Top-K Context Injection via Deferred Context Encoding for Non-Streaming ASR
Zelin Wu, Gan Song, Christopher Li +9
Contextual biasing enables speech recognizers to transcribe important phrases in the speaker's context, such as contact names, even if they are rare in, or absent from, the trainin…
IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages
Tahir Javed, Janki Atul Nawale, Eldho Ittan George +18
We present INDICVOICES, a dataset of natural and spontaneous speech containing a total of 7348 hours of read (9%), extempore (74%) and conversational (17%) audio from 16237 speaker…
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models
Rohit Prabhavalkar, Zhong Meng, Weiran Wang +7
The accuracy of end-to-end (E2E) automatic speech recognition (ASR) models continues to improve as they are scaled to larger sizes, with some now reaching billions of parameters. W…
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models
Shaojin Ding, David Qiu, David Rim +10
End-to-end automatic speech recognition (ASR) models have seen revolutionary quality gains with the recent development of large-scale universal speech models (USM). However, deploy…
Improved Long-Form Speech Recognition by Jointly Modeling the Primary and Non-primary Speakers
Guru Prakash Arumugam, Shuo-yiin Chang, Tara N. Sainath +3
ASR models often suffer from a long-form deletion problem where the model predicts sequential blanks instead of words when transcribing a lengthy audio (in the order of minutes or…
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
Lillian Zhou, Yuxin Ding, Mingqing Chen +5
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech. As language evolves and new terms come into use, these models can become ou…
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm
Weiran Wang, Zelin Wu, Diamantino Caseiro +10
Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scena…
Massive End-to-end Models for Short Search Queries
Weiran Wang, Rohit Prabhavalkar, Dongseong Hwang +11
In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for of…
Improving Joint Speech-Text Representations Without Alignment
Cal Peyser, Zhong Meng, Ke Hu +5
The last year has seen astonishing progress in text-prompted image generation premised on the idea of a cross-modal representation space in which the text and image domains are rep…
How to Estimate Model Transferability of Pre-Trained Speech Models?
Zih-Ching Chen, Chao-Han Huck Yang, Bo Li +6
In this work, we introduce a "score-based assessment" framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks. We leverage upo…
A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale
Cal Peyser, Michael Picheny, Kyunghyun Cho +3
Unpaired text and audio injection have emerged as dominant methods for improving ASR performance in the absence of a large labeled corpus. However, little guidance exists on deploy…
Lego-Features: Exporting modular encoder features for streaming and deliberation ASR
Rami Botros, Rohit Prabhavalkar, Johan Schalkwyk +3
In end-to-end (E2E) speech recognition models, a representational tight-coupling inevitably emerges between the encoder and the decoder. We build upon recent work that has begun to…
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models
Steven M. Hernandez, Ding Zhao, Shaojin Ding +5
Continued improvements in machine learning techniques offer exciting new opportunities through the use of larger models and larger training datasets. However, there is a growing ne…
End-to-End Speech Recognition: A Survey
Rohit Prabhavalkar, Takaaki Hori, Tara N. Sainath +2
In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning brought considerable reductions in word error rate of more than 50% relative, c…
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
Yu Zhang, Wei Han, James Qin +24
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the enc…
JEIT: Joint End-to-End Model and Internal Language Model Training for Speech Recognition
Zhong Meng, Weiran Wang, Rohit Prabhavalkar +7
We propose JEIT, a joint end-to-end (E2E) model and internal language model (ILM) training method to inject large-scale unpaired text into ILM during E2E training which improves ra…
From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition
Chao-Han Huck Yang, Bo Li, Yu Zhang +4
In this work, we propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition, which can \textbf{re-purpose} well-t…
Dual Learning for Large Vocabulary On-Device ASR
Cal Peyser, Ronny Huang, Tara Sainath +3
Dual learning is a paradigm for semi-supervised machine learning that seeks to leverage unsupervised data by solving two opposite tasks at once. In this scheme, each model is used…
E2E Segmentation in a Two-Pass Cascaded Encoder ASR Model
W. Ronny Huang, Shuo-Yiin Chang, Tara N. Sainath +7
We explore unifying a neural segmenter with two-pass cascaded encoder ASR into a single model. A key challenge is allowing the segmenter (which runs in real-time, synchronously wit…
JOIST: A Joint Speech and Text Streaming Model For ASR
Tara N. Sainath, Rohit Prabhavalkar, Ankur Bapna +6
We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E) model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous wo…
Modular Hybrid Autoregressive Transducer
Zhong Meng, Tongzhou Chen, Rohit Prabhavalkar +10
Text-only adaptation of a transducer model remains challenging for end-to-end speech recognition since the transducer has no clearly separated acoustic model (AM), language model (…