Search Results for author:
Found 60 papers, 25 papers with code
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System
We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development of a multi-channel, multi-speaker speech recognition system tailored to transcribe speech from distributed microphones and microphone arrays.
Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation
This capability offers a tailored training environment for developing neural models suited for speaker diarization and voice activity detection.
SelfVC: Voice Conversion With Iterative Refinement using Self Transformations
In this work, instead of explicitly disentangling attributes with loss terms, we present a framework to train a controllable voice conversion model on entangled speech representations derived from self-supervised learning and speaker verification models.
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation
We present a novel Speech Augmented Language Model (SALM) with {\em multitask} and {\em in-context} learning capabilities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition
Discrete audio representation, aka audio tokenization, has seen renewed interest driven by its potential to facilitate the application of text language modeling approaches in audio domain.
Investigating End-to-End ASR Architectures for Long Form Audio Transcription
This paper presents an overview and evaluation of some of the end-to-end ASR models on long-form audios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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).
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling
We study speech intent classification and slot filling (SICSF) by proposing to use an encoder pretrained on speech recognition (ASR) to initialize an end-to-end (E2E) Conformer-Transformer model, which achieves the new state-of-the-art results on the SLURP dataset, with 90. 14% intent accuracy and 82. 27% SLURP-F1.
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.
Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer
Code-Switching (CS) multilingual Automatic Speech Recognition (ASR) models can transcribe speech containing two or more alternating languages during a conversation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings
Contextual spelling correction models are an alternative to shallow fusion to improve automatic speech recognition (ASR) quality given user vocabulary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
Conformer-based models have become the dominant end-to-end architecture for speech processing tasks.
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
TDT models for Speech Recognition achieve better accuracy and up to 2. 82X faster inference than conventional Transducers.
Ranked #1 on
Slot Filling
on SLURP
Intent Classification
Intent Classification and Slot Filling
+3
Powerful and Extensible WFST Framework for RNN-Transducer Losses
This paper presents a framework based on Weighted Finite-State Transducers (WFST) to simplify the development of modifications for RNN-Transducer (RNN-T) loss.
VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation
We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system.
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
ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations
In this work, we propose a zero-shot voice conversion method using speech representations trained with self-supervised learning.
Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition
This paper presents a class of new fast non-trainable entropy-based confidence estimation methods for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models
In this paper, we extend previous self-supervised approaches for language identification by experimenting with Conformer based architecture in a multilingual pre-training paradigm.
Multi-blank Transducers for Speech Recognition
This paper proposes a modification to RNN-Transducer (RNN-T) models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers
In the proposed approach, a few small adapter modules are added to the original network.
A Compact End-to-End Model with Local and Global Context for Spoken Language Identification
We introduce TitaNet-LID, a compact end-to-end neural network for Spoken Language Identification (LID) that is based on the ContextNet architecture.
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
Thutmose Tagger: Single-pass neural model for Inverse Text Normalization
The model is trained on the Google Text Normalization dataset and achieves state-of-the-art sentence accuracy on both English and Russian test sets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments.
Ranked #4 on
Speech Synthesis
on LibriTTS
Multi-scale Speaker Diarization with Dynamic Scale Weighting
First, we use multi-scale clustering as an initialization to estimate the number of speakers and obtain the average speaker representation vector for each speaker and each scale.
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
First, a non-deterministic WFST outputs all normalization candidates, and then a neural language model picks the best one -- similar to shallow fusion for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adapting TTS models For New Speakers using Transfer Learning
We address this challenge by proposing transfer-learning guidelines for adapting high quality single-speaker TTS models for a new speaker, using only a few minutes of speech data.
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context
In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker representations.
Ranked #1 on
Speaker Diarization
on CH109
Mixer-TTS: non-autoregressive, fast and compact text-to-speech model conditioned on language model embeddings
This paper describes Mixer-TTS, a non-autoregressive model for mel-spectrogram generation.
CTC Variations Through New WFST Topologies
This paper presents novel Weighted Finite-State Transducer (WFST) topologies to implement Connectionist Temporal Classification (CTC)-like algorithms for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Unified Transformer-based Framework for Duplex Text Normalization
In addition, we also create a cleaned dataset from the Spoken Wikipedia Corpora for German and report the performance of our systems on the dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
CarneliNet: Neural Mixture Model for Automatic Speech Recognition
The basic idea is to introduce a parallel mixture of shallow networks instead of a very deep network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services
In this paper, we propose SGD-QA, a simple and extensible model for schema-guided dialogue state tracking based on a question answering approach.
TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction
We propose TalkNet, a non-autoregressive convolutional neural model for speech synthesis with explicit pitch and duration prediction.
NeMo Inverse Text Normalization: From Development To Production
Inverse text normalization (ITN) converts spoken-domain automatic speech recognition (ASR) output into written-domain text to improve the readability of the ASR output.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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)
+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 #2 on
Speech Recognition
on SPGISpeech
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
Hi-Fi Multi-Speaker English TTS Dataset
This paper introduces a new multi-speaker English dataset for training text-to-speech models.
On regularization of gradient descent, layer imbalance and flat minima
We analyze the training dynamics for deep linear networks using a new metric - layer imbalance - which defines the flatness of a solution.
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.
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model
In this work, we introduce a simple yet efficient post-processing model for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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 #30 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)
+2
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)
+3
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
Large Batch Training of Convolutional Networks with Layer-wise Adaptive Rate Scaling
Using LARS, we scaled AlexNet and ResNet-50 to a batch size of 16K.
Training Deep AutoEncoders for Recommender Systems
Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.
Mixed Precision Training
Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x.
Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification
However, it is not clear if these algorithms could replace BN in practical, large-scale applications.
Large Batch Training of Convolutional Networks
Using LARS, we scaled Alexnet up to a batch size of 8K, and Resnet-50 to a batch size of 32K without loss in accuracy.
Training Deep AutoEncoders for Collaborative Filtering
Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.
Factorization tricks for LSTM networks
We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and states into the independent groups.
Ranked #20 on
Language Modelling
on One Billion Word
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
SEBOOST applies a secondary optimization process in the subspace spanned by the last steps and descent directions.
