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 1501–1550 of 6433 papers

TitleStatusHype
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
Automatic Detection of Code-switching Style from Acoustics—0
Analyse de l'effet de la r\'everb\'eration sur la reconnaissance automatique de la parole (Analyzing how reverberation affects Automatic Speech Recognition)—0
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations—0
Automatic context window composition for distant speech recognition—0
An Alternative to Low-level-Sychrony-Based Methods for Speech Detection—0
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation—0
A Bayesian Approach to Estimation of Speaker Normalization Parameters—0
Automatic Classification of Spoken Languages using Diverse Acoustic Features—0
Automatic assessment of spoken language proficiency of non-native children—0
Automatic Assessment of Oral Reading Accuracy for Reading Diagnostics—0
Automatically Tailoring Unsupervised Morphological Segmentation to the Language—0
An affective computational model for machine consciousness—0
Automatically building a Tunisian Lexicon for Deverbal Nouns—0
Automatically Assess Children's Reading Skills—0
An Adaptive Psychoacoustic Model for Automatic Speech Recognition—0
Automated speech-unit delimitation in spoken learner English—0
Automated speech tools for helping communities process restricted-access corpora for language revival efforts—0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
Adapting OpenAI's Whisper for Speech Recognition on Code-Switch Mandarin-English SEAME and ASRU2019 Datasets—0
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition—0
Automated Speech Recognition Technology for Dialogue Interaction with Non-Native Interlocutors—0
Automated speech audiometry: Can it work using open-source pre-trained Kaldi-NL automatic speech recognition?—0
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement—0
Automated Speaker Independent Visual Speech Recognition: A Comprehensive Survey—0
Automated scoring of speaking items in an assessment for teachers of English as a Foreign Language—0
An adaptable task-oriented dialog system for stand-alone embedded devices—0
Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining—0
Automated scoring across different modalities—0
Automated Preamble Detection in Dictated Medical Reports—0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition—0
Automated Essay Scoring Based on Finite State Transducer: towards ASR Transcription of Oral English Speech—0
A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network Speech Enhancement—0
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers—0
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings—0
A baseline model for computationally inexpensive speech recognition for Kazakh using the Coqui STT framework—0
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation—0
Automated Cross-language Intelligibility Analysis of Parkinson's Disease Patients Using Speech Recognition Technologies—0
Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction—0
A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset—0
A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database—0
A user study to compare two conversational assistants designed for people with hearing impairments—0
A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation—0
A Multimodal Framework for Video Ads Understanding—0
A Unified Transformer-based Framework for Duplex Text Normalization—0
A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering—0
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition—0
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks—0
A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System—0
A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model—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
9Gated ConvNetsWord Error Rate (WER)4.8—Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN MPEPercentage error12.9—Unverified
9DNN MMIPercentage error12.9—Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
7Convolutional Speech RecognitionWord 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