SOTAVerified

Speech Recognition

Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: SpecAugment )

Papers

Showing 18511900 of 6433 papers

TitleStatusHype
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments0
CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR0
Curvature: A signature for Action Recognition in Video Sequences0
Curriculum Pre-training for End-to-End Speech Translation0
Attention-Guided Adaptation for Code-Switching Speech Recognition0
A Likelihood Ratio based Domain Adaptation Method for E2E Models0
Discourse-Based Modeling for AAC0
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool0
Adam^+: A Stochastic Method with Adaptive Variance Reduction0
Discovering Latent Structure in Task-Oriented Dialogues0
Curriculum optimization for low-resource speech recognition0
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm0
DiscreTalk: Text-to-Speech as a Machine Translation Problem0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition0
Current Challenges in Spoken Dialogue Systems and Why They Are Critical for Those Living with Dementia0
Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing0
Attention Enhanced Citrinet for Speech Recognition0
Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches0
Discriminative Joint Modeling of Lexical Variation and Acoustic Confusion for Automated Narrative Retelling Assessment0
CUNI Neural ASR with Phoneme-Level Intermediate Step for -Native at IWSLT 20200
Discriminative Pronunciation Modeling: A Large-Margin, Feature-Rich Approach0
Discriminative Segmental Cascades for Feature-Rich Phone Recognition0
Discriminative Self-training for Punctuation Prediction0
Discriminative Speech Recognition Rescoring with Pre-trained Language Models0
Discriminative state tracking for spoken dialog systems0
Discriminative training of RNNLMs with the average word error criterion0
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective0
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation0
Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion0
Disentangling Prosody Representations with Unsupervised Speech Reconstruction0
Cumulative Adaptation for BLSTM Acoustic Models0
Attention-based Wav2Text with Feature Transfer Learning0
Disfluency Correction using Unsupervised and Semi-supervised Learning0
Disfluency Detection with Unlabeled Data and Small BERT Models0
DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction0
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition0
Distillation Strategies for Discriminative Speech Recognition Rescoring0
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation0
Distilling HuBERT with LSTMs via Decoupled Knowledge Distillation0
CTC Variations Through New WFST Topologies0
Attention-based Transducer for Online Speech Recognition0
Distilling the Knowledge of BERT for CTC-based ASR0
CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition0
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model0
CTC Blank Triggered Dynamic Layer-Skipping for Efficient CTC-based Speech Recognition0
Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English0
Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework0
Distinguishing Common and Proper Nouns0
Distributed Deep Learning Strategies For Automatic Speech Recognition0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified