Search Results for author:
Found 39 papers, 1 papers with code
Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data
Without any transcribed speech in a new language, this TTS model can generate intelligible speech in >30 unseen languages (CER difference of <10% to ground truth).
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
O-1: Self-training with Oracle and 1-best Hypothesis
O-1 achieves 13\% to 25\% relative improvement over EMBR on the various datasets that SpeechStew comprises of, and a 12\% relative gap reduction with respect to the oracle WER over EMBR training on the in-house dataset.
Large-scale Language Model Rescoring on Long-form Data
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.
Understanding Shared Speech-Text Representations
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 recognition (ASR)and Speech Translation (ST) performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Robust Knowledge Distillation from RNN-T Models With Noisy Training Labels Using Full-Sum Loss
Soft distillation is another popular KD method that distills the output logits of the teacher 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
JEIT: Joint End-to-End Model and Internal Language Model Training for Speech Recognition
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 rare-word speech recognition.
Modular Hybrid Autoregressive Transducer
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.
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR
Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training.
Ranked #1 on
Speech Recognition
on CHiME-6 eval
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR
First, we show that by combining speech representations with byte-level text representations and use of language embeddings, we can dramatically reduce the Character Error Rate (CER) on languages with no supervised speech from 64. 8\% to 30. 8\%, a relative reduction of 53\%.
Non-Parallel Voice Conversion for ASR Augmentation
For ASR augmentation, it is necessary that the VC model be robust to a wide range of input speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition
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
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data
However, there are not enough data sets for accented speech, and for the ones that are already available, more training approaches need to be explored to improve the quality of accented speech recognition.
Improving Rare Word Recognition with LM-aware MWER Training
Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the rescoring setups.
MAESTRO: Matched Speech Text Representations through Modality Matching
Self-supervised learning from speech signals aims to learn the latent structure inherent in the signal, while self-supervised learning from text attempts to capture lexical information.
Ask2Mask: Guided Data Selection for Masked Speech Modeling
They treat all unsupervised speech samples with equal weight, which hinders learning as not all samples have relevant information to learn meaningful representations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio.
Ranked #1 on
Speech Recognition
on Common Voice
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Injecting Text in Self-Supervised Speech Pretraining
The proposed method, tts4pretrain complements the power of contrastive learning in self-supervision with linguistic/lexical representations derived from synthesized speech, effectively learning from untranscribed speech and unspoken text.
LSTM Acoustic Models Learn to Align and Pronounce with Graphemes
Automated speech recognition coverage of the world's languages continues to expand.
Language-agnostic Multilingual Modeling
Multilingual Automated Speech Recognition (ASR) systems allow for the joint training of data-rich and data-scarce languages in a single model.
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior
Recent neural text-to-speech (TTS) models with fine-grained latent features enable precise control of the prosody of synthesized speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Speech Recognition with Augmented Synthesized Speech
Recent success of the Tacotron speech synthesis architecture and its variants in producing natural sounding multi-speaker synthesized speech has raised the exciting possibility of replacing expensive, manually transcribed, domain-specific, human speech that is used to train speech recognizers.
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model
Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic speech recognition (ASR) coverage of the world's languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages.
Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition
The performance of automatic speech recognition systems degrades with increasing mismatch between the training and testing scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Building competitive direct acoustics-to-word models for English conversational speech recognition
This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Language Modeling with Highway LSTM
Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Direct Acoustics-to-Word Models for English Conversational Speech Recognition
Our CTC word model achieves a word error rate of 13. 0%/18. 8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or decoder compared with 9. 6%/16. 0% for phone-based CTC with a 4-gram LM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
English Conversational Telephone Speech Recognition by Humans and Machines
This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates?
Ranked #3 on
Speech Recognition
on Switchboard + Hub500
End-to-End ASR-free Keyword Search from Speech
The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Invariant Representations for Noisy Speech Recognition
Ensuring such robustness to variability is a challenge in modern day neural network-based ASR systems, especially when all types of variability are not seen during training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Training variance and performance evaluation of neural networks in speech
In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Diverse Embedding Neural Network Language Models
We propose Diverse Embedding Neural Network (DENN), a novel architecture for language models (LMs).
Generalized Ambiguity Decomposition for Understanding Ensemble Diversity
We present extensions of this decomposition to common regression and classification loss functions, and report a simulation-based analysis of the diversity term and the accuracy of the decomposition.
Accelerating Hessian-free optimization for deep neural networks by implicit preconditioning and sampling
This study aims at speeding up Hessian-free training, both by means of decreasing the amount of data used for training, as well as through reduction of the number of Krylov subspace solver iterations used for implicit estimation of the Hessian.
Improvements to deep convolutional neural networks for LVCSR
We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline.
