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 32013250 of 6433 papers

TitleStatusHype
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling0
Automatic Speech Recognition Advancements for Indigenous Languages of the Americas0
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition0
ASR-Aware End-to-end Neural Diarization0
ASR-based CALL systems and learner speech data: new resources and opportunities for research and development in second language learning0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach0
ASR Bundestag: A Large-Scale political debate dataset in German0
ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction0
ASR Error Correction and Domain Adaptation Using Machine Translation0
ASR Error Correction using Large Language Models0
ASR Error Detection via Audio-Transcript entailment0
ASR error management for improving spoken language understanding0
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems0
ASR for Documenting Acutely Under-Resourced Indigenous Languages0
ASR for Non-standardised Languages with Dialectal Variation: the case of Swiss German0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding0
ASR in German: A Detailed Error Analysis0
ASR is all you need: cross-modal distillation for lip reading0
ASR Rescoring and Confidence Estimation with ELECTRA0
Assessing ASR Model Quality on Disordered Speech using BERTScore0
Assessing Relative Sentence Complexity using an Incremental CCG Parser0
Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows0
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors0
Assessment of ESL Learners' Syntactic Competence Based on Similarity Measures0
Assessment of Non-native Prosody for Spanish as L2 using quantitative scores and perceptual evaluation0
ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers0
ASTRA: Aligning Speech and Text Representations for Asr without Sampling0
A Streaming End-to-End Framework For Spoken Language Understanding0
CUSIDE-array: A Streaming Multi-Channel End-to-End Speech Recognition System with Realistic Evaluations0
Multi-Sentence Grounding for Long-term Instructional Video0
A Study into Pre-training Strategies for Spoken Language Understanding on Dysarthric Speech0
A Study of All-Convolutional Encoders for Connectionist Temporal Classification0
A Study of BFLOAT16 for Deep Learning Training0
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition0
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems0
A Study of Language Modeling for Chinese Spelling Check0
A study of latent monotonic attention variants0
A Study of Non-autoregressive Model for Sequence Generation0
A Study on Lip Localization Techniques used for Lip reading from a Video0
A study on native American English speech recognition by Indian listeners with varying word familiarity level0
表示法學習技術於節錄式語音文件摘要之研究(A Study on Representation Learning Techniques for Extractive Spoken Document Summarization) [In Chinese]0
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge0
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding0
A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment0
A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models0
A Subband-Based SVM Front-End for Robust ASR0
A Supervised STDP-based Training Algorithm for Living Neural Networks0
A Survey of Multilingual Models for Automatic Speech Recognition0
A Survey of the Recent Architectures of Deep Convolutional Neural Networks0
Towards a Robust Deep Neural Network in Texts: A Survey0
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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