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 29012950 of 6433 papers

TitleStatusHype
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement0
Knowledge distillation from language model to acoustic model: a hierarchical multi-task learning approach0
Progressive Learning for Stabilizing Label Selection in Speech Separation with Mapping-based Method0
An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR0
Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition0
AequeVox: Automated Fairness Testing of Speech Recognition SystemsCode0
Personalized Speech Enhancement: New Models and Comprehensive Evaluation0
Similarity-and-Independence-Aware Beamformer with Iterative Casting and Boost Start for Target Source Extraction Using Reference0
Automatic Learning of Subword Dependent Model Scales0
Analysis of French Phonetic Idiosyncrasies for Accent RecognitionCode0
ViraPart: A Text Refinement Framework for Automatic Speech Recognition and Natural Language Processing Tasks in Persian0
Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages0
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
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes0
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet0
Scribosermo: Fast Speech-to-Text models for German and other LanguagesCode0
Towards Identity Preserving Normal to Dysarthric Voice Conversion0
Advances and Challenges in Deep Lip Reading0
CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian PortugueseCode1
Sub-word Level Lip Reading With Visual Attention0
Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts0
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language ProcessingCode1
All-neural beamformer for continuous speech separation0
Continual learning using lattice-free MMI for speech recognition0
Prompt-tuning in ASR systems for efficient domain-adaptation0
Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks0
On Language Model Integration for RNN Transducer based Speech Recognition0
Multi-Modal Pre-Training for Automated Speech Recognition0
UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-TrainingCode1
Word Order Does Not Matter For Speech Recognition0
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models0
Speech Summarization using Restricted Self-Attention0
Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognitionCode1
LightSeq2: Accelerated Training for Transformer-based Models on GPUsCode2
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control CommunicationsCode1
Partial Variable Training for Efficient On-Device Federated Learning0
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition0
Interactive Feature Fusion for End-to-End Noise-Robust Speech RecognitionCode1
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation0
K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and SyllablesCode1
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy0
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition0
Long Expressive Memory for Sequence ModelingCode1
Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding0
Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition0
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation0
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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