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 31513200 of 6433 papers

TitleStatusHype
An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR0
AequeVox: Automated Fairness Testing of Speech Recognition SystemsCode0
Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition0
ViraPart: A Text Refinement Framework for Automatic Speech Recognition and Natural Language Processing Tasks in Persian0
Automatic Learning of Subword Dependent Model Scales0
Analysis of French Phonetic Idiosyncrasies for Accent RecognitionCode0
Similarity-and-Independence-Aware Beamformer with Iterative Casting and Boost Start for Target Source Extraction Using Reference0
Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages0
Personalized Speech Enhancement: New Models and Comprehensive Evaluation0
Efficient Sequence Training of Attention Models using Approximative Recombination0
OkwuGbé: End-to-End Speech Recognition for Fon and Igbo0
Towards Robust Waveform-Based Acoustic Models0
A Unified Speaker Adaptation Approach for ASRCode0
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes0
Towards Identity Preserving Normal to Dysarthric Voice Conversion0
Scribosermo: Fast Speech-to-Text models for German and other LanguagesCode0
Advances and Challenges in Deep Lip Reading0
Sub-word Level Lip Reading With Visual Attention0
Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts0
Prompt-tuning in ASR systems for efficient domain-adaptation0
Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks0
All-neural beamformer for continuous speech separation0
Continual learning using lattice-free MMI for speech recognition0
On Language Model Integration for RNN Transducer based Speech Recognition0
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models0
Multi-Modal Pre-Training for Automated Speech Recognition0
Word Order Does Not Matter For Speech Recognition0
Speech Summarization using Restricted Self-Attention0
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition0
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition0
Partial Variable Training for Efficient On-Device Federated Learning0
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy0
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric0
Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain0
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation0
Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition0
Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition0
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR0
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets0
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition0
Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition0
Improving Pseudo-label Training For End-to-end Speech Recognition Using Gradient Mask0
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition0
Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training0
Explaining the Attention Mechanism of End-to-End Speech Recognition Using Decision Trees0
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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