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

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Showing 5551–5600 of 6433 papers

TitleStatusHype
An ASR-free Fluency Scoring Approach with Self-Supervised Learning—0
Anatomy of Industrial Scale Multilingual ASR—0
An Attentional Model for Speech Translation Without Transcription—0
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering—0
An automated medical scribe for documenting clinical encounters—0
Anchored Speech Recognition with Neural Transducers—0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution—0
An Effective Context-Balanced Adaptation Approach for Long-Tailed Speech Recognition—0
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features—0
An Effective End-to-End Modeling Approach for Mispronunciation Detection—0
An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement—0
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts—0
An Effective Training Framework for Light-Weight Automatic Speech Recognition Models—0
An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation—0
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks—0
An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems—0
An efficient text augmentation approach for contextualized Mandarin speech recognition—0
An Empirical Analysis of Deep Audio-Visual Models for Speech Recognition—0
An empirical assessment of deep learning approaches to task-oriented dialog management—0
An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition—0
An Empirical Study of Efficient ASR Rescoring with Transformers—0
An Empirical Study of Language Model Integration for Transducer based Speech Recognition—0
An End-to-end Architecture of Online Multi-channel Speech Separation—0
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling—0
An End-to-End Speech Recognition for the Nepali Language—0
An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network—0
An enhanced automatic speech recognition system for Arabic—0
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition—0
A network of deep neural networks for distant speech recognition—0
A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer And Large Scale Synthetic Data—0
A neural document language modeling framework for spoken document retrieval—0
A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer—0
An evaluation of word-level confidence estimation for end-to-end automatic speech recognition—0
A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain—0
A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning—0
A New Form of Humor --- Mapping Constraint-Based Computational Morphologies to a Finite-State Representation—0
An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR—0
An experimental analysis of Noise-Contrastive Estimation: the noise distribution matters—0
An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings—0
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition—0
An Experiment on Speech-to-Text Translation Systems for Manipuri to English on Low Resource Setting—0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks—0
An explicit statistical model of learning lexical segmentation using multiple cues—0
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition—0
An Extension of the Slovak Broadcast News Corpus based on Semi-Automatic Annotation—0
Animal inspired Application of a Variant of Mel Spectrogram for Seismic Data Processing—0
An Improved Hierarchical Word Sequence Language Model Using Directional Information—0
An Improved Residual LSTM Architecture for Acoustic Modeling—0
An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition—0
An inclusive review on deep learning techniques and their scope in handwriting recognition—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified