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Found 24 papers, 2 papers with code
Federated Representation Learning for Automatic Speech Recognition
Federated Learning (FL) is a privacy-preserving paradigm, allowing edge devices to learn collaboratively without sharing data.
Federated Self-Learning with Weak Supervision for Speech Recognition
Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech
By fine-tuning an ASR model on synthetic stuttered speech we are able to reduce word error by 5. 7% relative on stuttered utterances, with only minor (<0. 2% relative) degradation for fluent utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Guided contrastive self-supervised pre-training for automatic speech recognition
Contrastive Predictive Coding (CPC) is a representation learning method that maximizes the mutual information between intermediate latent representations and the output of a given model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale
For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation
A popular approach is to fine-tune the model with data from regions where the ASR model has a higher word error rate (WER).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adversarial Reweighting for Speaker Verification Fairness
Results show that the pairwise weighting method can achieve 1. 08% overall EER, 1. 25% for male and 0. 67% for female speakers, with relative EER reductions of 7. 7%, 10. 1% and 3. 0%, respectively.
Improving fairness in speaker verification via Group-adapted Fusion Network
This is observed especially with underrepresented demographic groups sharing similar voice characteristics.
Investigation of Training Label Error Impact on RNN-T
In this paper, we propose an approach to quantitatively analyze impacts of different training label errors to RNN-T based ASR models.
SynthASR: Unlocking Synthetic Data for Speech Recognition
Our observations show that SynthASR holds great promise in training the state-of-the-art large-scale E2E ASR models for new applications while reducing the costs and dependency on production data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CoDERT: Distilling Encoder Representations with Co-learning for Transducer-based Speech Recognition
We find that tandem training of teacher and student encoders with an inplace encoder distillation outperforms the use of a pre-trained and static teacher transducer.
Scaling Laws for Acoustic Models
We extend previous work to jointly predict loss due to model size, to training set size, and to the inherent "irreducible loss" of the task.
Improving multi-speaker TTS prosody variance with a residual encoder and normalizing flows
This paper proposes a new neural text-to-speech model that approaches the disentanglement problem by conditioning a Tacotron2-like architecture on flow-normalized speaker embeddings, and by substituting the reference encoder with a new learned latent distribution responsible for modeling the intra-sentence variability due to the prosody.
Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio
An ASR model that operates on both primary and auxiliary data can achieve better accuracy compared to a primary-only solution; and a model that can serve both primary-only (PO) and primary-plus-auxiliary (PPA) modes is highly desirable.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End
On the other hand, a streaming system using per-frame intent posteriors as extra inputs for the RNN-T ASR system yields a 3. 33% relative WERR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition
The end-to-end 2D Conv-Attention model is compared with a multi-head self-attention and superdirective-based neural beamformers.
Wav2vec-C: A Self-supervised Model for Speech Representation Learning
However, the quantization process is regularized by an additional consistency network that learns to reconstruct the input features to the wav2vec 2. 0 network from the quantized representations in a way similar to a VQ-VAE model.
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding
Spoken language understanding (SLU) systems extract transcriptions, as well as semantics of intent or named entities from speech, and are essential components of voice activated systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS).
Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognition
Unlike previous work on this topic, which performs on-the-fly limited-size beam-search decoding and generates alignment scores for expected edit-distance computation, in our proposed method, we re-calculate and sum scores of all the possible alignments for each hypothesis in N-best lists.
Acoustic-To-Word Model Without OOV
However, this type of word-based CTC model suffers from the out-of-vocabulary (OOV) issue as it can only model limited number of words in the output layer and maps all the remaining words into an OOV output node.
Comparing Human and Machine Errors in Conversational Speech Transcription
In this paper we approach this question by comparing the output of our most accurate CTS recognition system to that of a standard speech transcription vendor pipeline.
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition
We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation
We propose to train bi-directional neural network language model(NNLM) with noise contrastive estimation(NCE).
