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 5301–5350 of 6433 papers

TitleStatusHype
Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices—0
Self-Teaching Networks—0
Self-Training for End-to-End Speech Recognition—0
Self-Training for End-to-End Speech Translation—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions—0
Semantic Communications for Speech Recognition—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—0
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding—0
Semantic Language Model for Tunisian Dialect—0
Semantic parsing of speech using grammars learned with weak supervision—0
Semantic-preserved Communication System for Highly Efficient Speech Transmission—0
Semantic Role Labeling Improves Incremental Parsing—0
Semantic sentence similarity: size does not always matter—0
Semantics for Large-Scale Multimedia: New Challenges for NLP—0
Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction—0
Semantic-WER: A Unified Metric for the Evaluation of ASR Transcript for End Usability—0
SeMaScore : a new evaluation metric for automatic speech recognition tasks—0
SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge—0
Semi-automatic annotation of the UCU accents speech corpus—0
Semi-Autoregressive Streaming ASR With Label Context—0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition—0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition—0
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech—0
Semi-supervised acoustic model training for speech with code-switching—0
Semi-supervised ASR by End-to-end Self-training—0
Semi-Supervised Discriminative Language Modeling with Out-of-Domain Text Data—0
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter—0
Semi-supervised Learning with Sparse Autoencoders in Phone Classification—0
Semi-Supervised Model Training for Unbounded Conversational Speech Recognition—0
Semi-Supervised Speech Recognition via Graph-based Temporal Classification—0
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages—0
Semi-tied Units for Efficient Gating in LSTM and Highway Networks—0
SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors—0
Senone-aware Adversarial Multi-task Training for Unsupervised Child to Adult Speech Adaptation—0
Sentence Boundary Augmentation For Neural Machine Translation Robustness—0
Sentence Compression For Automatic Subtitling—0
Sentence segmentation of aphasic speech—0
Sentence selection for automatic scoring of Mandarin proficiency—0
Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition—0
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities—0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter—0
SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation—0
Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speech—0
Sequence-based Multi-lingual Low Resource Speech Recognition—0
Sequence Discriminative Training for Deep Learning based Acoustic Keyword Spotting—0
Sequence-level Confidence Classifier for ASR Utterance Accuracy and Application to Acoustic Models—0
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition—0
Sequence-level self-learning with multiple hypotheses—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