Search Results for author:
Found 13 papers, 2 papers with code
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Universal Automatic Phonetic Transcription into the International Phonetic Alphabet
This paper presents a state-of-the-art model for transcribing speech in any language into the International Phonetic Alphabet (IPA).
Toward domain-invariant speech recognition via large scale training
More importantly, such models generalize better to unseen conditions and allow for rapid adaptation -- we show that by using as little as 10 hours of data from a new domain, an adapted domain-invariant model can match performance of a domain-specific model trained from scratch using 70 times as much data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
From Audio to Semantics: Approaches to end-to-end spoken language understanding
Conventional spoken language understanding systems consist of two main components: an automatic speech recognition module that converts audio to a transcript, and a natural language understanding module that transforms the resulting text (or top N hypotheses) into a set of domains, intents, and arguments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Scaling End-to-End Models for Large-Scale Multilingual ASR
Building ASR models across many languages is a challenging multi-task learning problem due to large variations and heavily unbalanced data.
Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition
Our experiments show that for both losses, the WER on long-form speech reduces substantially as the training utterance length increases.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Unsupervised Data Selection via Discrete Speech Representation for ASR
Self-supervised learning of speech representations has achieved impressive results in improving automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Language Agnostic Multilingual Streaming On-Device ASR System
On-device end-to-end (E2E) models have shown improvements over a conventional model on English Voice Search tasks in both quality and latency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification
Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Accelerating RNN-T Training and Inference Using CTC guidance
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 classification (CTC) model.
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Using Text Injection to Improve Recognition of Personal Identifiers in Speech
We use text-injection to improve the recognition of PII categories by including fake textual substitutes of PII categories in the training data using a text injection method.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition
Adapters are an efficient, composable alternative to full fine-tuning of pre-trained models and help scale the deployment of large ASR models to many tasks.
