113 citations · 133 across the 14 of their papers we have counts for
14 papers
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…
High-precision Voice Search Query Correction via Retrievable Speech-text Embedings
Christopher Li, Gary Wang, Kyle Kastner +9
Automatic speech recognition (ASR) systems can suffer from poor recall for various reasons, such as noisy audio, lack of sufficient training data, etc. Previous work has shown that…
Using Text Injection to Improve Recognition of Personal Identifiers in Speech
Yochai Blau, Rohan Agrawal, Lior Madmony +7
Accurate recognition of specific categories, such as persons' names, dates or other identifiers is critical in many Automatic Speech Recognition (ASR) applications. As these catego…
Understanding Shared Speech-Text Representations
Gary Wang, Kyle Kastner, Ankur Bapna +4
Recently, a number of approaches to train speech models by incorpo-rating text into end-to-end models have been developed, with Mae-stro advancing state-of-the-art automatic speech…
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…
Accelerating RNN-T Training and Inference Using CTC guidance
Yongqiang Wang, Zhehuai Chen, Chengjian Zheng +3
We propose a novel method to accelerate training and inference process of recurrent neural network transducer (RNN-T) based on the guidance from a co-trained connectionist temporal…
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR
Zhehuai Chen, Ankur Bapna, Andrew Rosenberg +4
Training state-of-the-art Automated Speech Recognition (ASR) models typically requires a substantial amount of transcribed speech. In this work, we demonstrate that a modality-matc…
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…
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
Takaaki Saeki, Heiga Zen, Zhehuai Chen +6
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models. Existing multilingual TTS typ…
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data
Alëna Aksënova, Zhehuai Chen, Chung-Cheng Chiu +8
Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work fo…
Unsupervised Data Selection via Discrete Speech Representation for ASR
Zhiyun Lu, Yongqiang Wang, Yu Zhang +3
Self-supervised learning of speech representations has achieved impressive results in improving automatic speech recognition (ASR). In this paper, we show that data selection is im…
MAESTRO: Matched Speech Text Representations through Modality Matching
Zhehuai Chen, Yu Zhang, Andrew Rosenberg +4
We present Maestro, a self-supervised training method to unify representations learnt from speech and text modalities. Self-supervised learning from speech signals aims to learn th…
Injecting Text in Self-Supervised Speech Pretraining
Zhehuai Chen, Yu Zhang, Andrew Rosenberg +3
Self-supervised pretraining for Automated Speech Recognition (ASR) has shown varied degrees of success. In this paper, we propose to jointly learn representations during pretrainin…
End-to-end contextual speech recognition using class language models and a token passing decoder
Zhehuai Chen, Mahaveer Jain, Yongqiang Wang +2
End-to-end modeling (E2E) of automatic speech recognition (ASR) blends all the components of a traditional speech recognition system into a unified model. Although it simplifies tr…