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 16011650 of 6433 papers

TitleStatusHype
Explainability of Speech Recognition Transformers via Gradient-based Attention VisualizationCode0
Streaming Speech-to-Confusion Network Speech Recognition0
Can Contextual Biasing Remain Effective with Whisper and GPT-2?Code1
Audio-Visual Speech Enhancement with Score-Based Generative Models0
DistilXLSR: A Light Weight Cross-Lingual Speech Representation ModelCode1
Tensor decomposition for minimization of E2E SLU model toward on-device processing0
Improved Training for End-to-End Streaming Automatic Speech Recognition Model with Punctuation0
Improved DeepFake Detection Using Whisper FeaturesCode1
On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms0
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset0
On the Robustness of Arabic Speech Dialect Identification0
Adapting an Unadaptable ASR System0
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts0
Enhancing the Unified Streaming and Non-streaming Model with Contrastive Learning0
Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home0
Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition0
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations0
Adaptation and Optimization of Automatic Speech Recognition (ASR) for the Maritime Domain in the Field of VHF Communication0
Speech inpainting: Context-based speech synthesis guided by video0
AfriNames: Most ASR models "butcher" African Names0
Encoder-decoder multimodal speaker change detection0
SlothSpeech: Denial-of-service Attack Against Speech Recognition ModelsCode0
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili0
Strategies for improving low resource speech to text translation relying on pre-trained ASR models0
The Tag-Team Approach: Leveraging CLS and Language Tagging for Enhancing Multilingual ASR0
Accurate and Structured Pruning for Efficient Automatic Speech Recognition0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning0
Zero-Shot Automatic Pronunciation Assessment0
Perception and Semantic Aware Regularization for Sequential Confidence CalibrationCode1
VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition0
The News Delivery Channel Recommendation Based on Granular Neural Network0
Towards Selection of Text-to-speech Data to Augment ASR Training0
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers0
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions0
Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer GeneratorCode0
Building Accurate Low Latency ASR for Streaming Voice Search0
Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target0
Retraining-free Customized ASR for Enharmonic Words Based on a Named-Entity-Aware Model and Phoneme Similarity Estimation0
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition0
An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings0
HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition0
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice0
RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition0
Bridging the Granularity Gap for Acoustic ModelingCode1
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU0
BIG-C: a Multimodal Multi-Purpose Dataset for BembaCode1
Robustness of Multi-Source MT to Transcription Errors0
2-bit Conformer quantization for automatic speech recognition0
DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction0
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distributionCode0
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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