Search Results for author:
Found 27 papers, 10 papers with code
Training and Inference Efficiency of Encoder-Decoder Speech Models
However, the reported data and compute requirements for their training are prohibitive for many in the research community.
TTS-Transducer: End-to-End Speech Synthesis with Neural Transducer
The proposed system first uses a transducer architecture to learn monotonic alignments between tokenized text and speech codec tokens for the first codebook.
Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages
This study explores methods to increase data volume for low-resource languages using techniques such as crowdsourcing, pseudo-labeling, advanced data preprocessing and various permissive data sources such as audiobooks, Common Voice, YouTube.
Anticipating Future with Large Language Model for Simultaneous Machine Translation
Simultaneous machine translation (SMT) takes streaming input utterances and incrementally produces target text.
EMMeTT: Efficient Multimodal Machine Translation Training
This work focuses on neural machine translation (NMT) and proposes a joint multimodal training regime of Speech-LLM to include automatic speech translation (AST).
Chain-of-Thought Prompting for Speech Translation
Building on the success of text-based LLMs, recent research has adapted these models to use speech embeddings for prompting, resulting in Speech-LLM models that exhibit strong performance in automatic speech recognition (ASR) and automatic speech translation (AST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Less is More: Accurate Speech Recognition & Translation without Web-Scale Data
Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data.
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter
Accurate recognition of rare and new words remains a pressing problem for contextualized Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Label-Looping: Highly Efficient Decoding for Transducers
This paper introduces a highly efficient greedy decoding algorithm for Transducer-based speech recognition models.
LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of end-to-end ASR Models
Traditional automatic speech recognition (ASR) models output lower-cased words without punctuation marks, which reduces readability and necessitates a subsequent text processing model to convert ASR transcripts into a proper format.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Chat About Boring Problems: Studying GPT-based text normalization
Through this new framework, we can identify strengths and weaknesses of GPT-based TN, opening opportunities for future work.
Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio
We propose CONF-TSASR, a non-autoregressive end-to-end time-frequency domain architecture for single-channel target-speaker automatic speech recognition (TS-ASR).
Confidence-based Ensembles of End-to-End Speech Recognition Models
Second, we demonstrate that it is possible to combine base and adapted models to achieve strong results on both original and target data.
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator
We propose an end-to-end Automatic Speech Recognition (ASR) system that can be trained on transcribed speech data, text-only data, or a mixture of both.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition
Automatic speech recognition models are often adapted to improve their accuracy in a new domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Toolbox for Construction and Analysis of Speech Datasets
Automatic Speech Recognition and Text-to-Speech systems are primarily trained in a supervised fashion and require high-quality, accurately labeled speech datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition
We propose Citrinet - a new end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition
In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models.
Ranked #3 on
Speech Recognition
on SPGISpeech
Hi-Fi Multi-Speaker English TTS Dataset
This paper introduces a new multi-speaker English dataset for training text-to-speech models.
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments
We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay.
QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions
We propose a new end-to-end neural acoustic model for automatic speech recognition.
Ranked #41 on
Speech Recognition
on LibriSpeech test-clean
Speech Recognition
Audio and Speech Processing
NeMo: a toolkit for building AI applications using Neural Modules
NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition.
Ranked #1 on
Speech Recognition
on Common Voice Spanish
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks
We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay.
Jasper: An End-to-End Convolutional Neural Acoustic Model
In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data.
Ranked #3 on
Speech Recognition
on Hub5'00 SwitchBoard
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation
Building an accurate automatic speech recognition (ASR) system requires a large dataset that contains many hours of labeled speech samples produced by a diverse set of speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models
We present OpenSeq2Seq {--} an open-source toolkit for training sequence-to-sequence models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
We present OpenSeq2Seq - a TensorFlow-based toolkit for training sequence-to-sequence models that features distributed and mixed-precision training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
