184 citations · 229 across the 10 of their papers we have counts for
11 papers
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…
SLM: Bridge the thin gap between speech and text foundation models
Mingqiu Wang, Wei Han, Izhak Shafran +15
We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models. SLM…
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…
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion
Cal Peyser, Tara N. Sainath, Golan Pundak
Proper nouns present a challenge for end-to-end (E2E) automatic speech recognition (ASR) systems in that a particular name may appear only rarely during training, and may have a pr…
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Tara N. Sainath, Yanzhang He, Bo Li +26
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e…
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models
Ke Hu, Antoine Bruguier, Tara N. Sainath +2
Contextual automatic speech recognition, i.e., biasing recognition towards a given context (e.g. user's playlists, or contacts), is challenging in end-to-end (E2E) models. Such mod…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
Streaming End-to-end Speech Recognition For Mobile Devices
Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar +17
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present nu…
Contextual Speech Recognition with Difficult Negative Training Examples
Uri Alon, Golan Pundak, Tara N. Sainath
Improving the representation of contextual information is key to unlocking the potential of end-to-end (E2E) automatic speech recognition (ASR). In this work, we present a novel an…
Toward domain-invariant speech recognition via large scale training
Arun Narayanan, Ananya Misra, Khe Chai Sim +6
Current state-of-the-art automatic speech recognition systems are trained to work in specific `domains', defined based on factors like application, sampling rate and codec. When su…
Deep context: end-to-end contextual speech recognition
Golan Pundak, Tara N. Sainath, Rohit Prabhavalkar +2
In automatic speech recognition (ASR) what a user says depends on the particular context she is in. Typically, this context is represented as a set of word n-grams. In this work, w…