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 23012350 of 6433 papers

TitleStatusHype
Evaluating Automatic Speech Recognition in Translation0
Evaluating Automatic Speech Recognition in an Incremental Setting0
Evaluating Compound Splitters Extrinsically with Textual Entailment0
Building Open-source Speech Technology for Low-resource Minority Languages with SáMi as an Example – Tools, Methods and Experiments0
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis0
Evaluating Low-Level Speech Features Against Human Perceptual Data0
Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person0
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline0
Building Text-To-Speech Voices in the Cloud0
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text0
A Novel Topology for End-to-end Temporal Classification and Segmentation with Recurrent Neural Network0
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance0
Evaluating the Communication Efficiency in Federated Learning Algorithms0
Evaluating the Performance of a Speech Recognition based System0
Survey of End-to-End Multi-Speaker Automatic Speech Recognition for Monaural Audio0
Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group0
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric0
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept0
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts0
Evaluation of acoustic word embeddings0
Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective0
Evaluation of Automatic Speech Recognition for Conversational Speech in Dutch, English and German: What Goes Missing?0
Evaluation of Crowdsourced User Input Data for Spoken Dialog Systems0
Cache-Augmented Latent Topic Language Models for Speech Retrieval0
Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models0
Evaluation of LLMs in Speech is Often Flawed: Test Set Contamination in Large Language Models for Speech Recognition0
CAFE A Novel Code switching Dataset for Algerian Dialect French and English0
Evaluation of Off-the-shelf Speech Recognizers Across Diverse Dialogue Domains0
BSTC: A Large-Scale Chinese-English Speech Translation Dataset0
Evaluation of real-time transcriptions using end-to-end ASR models0
Evaluation of Speaker Anonymization on Emotional Speech0
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection0
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks0
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment0
Evolutionary optimization of contexts for phonetic correction in speech recognition systems0
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN0
Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down0
Evolving Learning Rate Optimizers for Deep Neural Networks0
BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators0
A Novel Speech-Driven Lip-Sync Model with CNN and LSTM0
Environment-aware Reconfigurable Noise Suppression0
Experimental Study of Vowels in Nagamese, Ao and Lotha: Languages of Nagaland0
Experiments of ASR-based mispronunciation detection for children and adult English learners0
Can Generative Large Language Models Perform ASR Error Correction?0
Environmental Noise Embeddings for Robust Speech Recognition0
Explaining Deep Neural Networks0
Explaining the Attention Mechanism of End-to-End Speech Recognition Using Decision Trees0
Entity resolution for noisy ASR transcripts0
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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