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 4751–4800 of 6433 papers

TitleStatusHype
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition—0
On the limit of English conversational speech recognition—0
On the N-gram Approximation of Pre-trained Language Models—0
On the Relation between Internal Language Model and Sequence Discriminative Training for Neural Transducers—0
On the Relevance of Auditory-Based Gabor Features for Deep Learning in Automatic Speech Recognition—0
On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition—0
On the Robust Approximation of ASR Metrics—0
On the Robustness of Arabic Speech Dialect Identification—0
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks—0
On the Transferability of Whisper-based Representations for "In-the-Wild" Cross-Task Downstream Speech Applications—0
On the Usefulness of Self-Attention for Automatic Speech Recognition with Transformers—0
On the use of a fuzzy classifier to speed up the Sp\_ToBI labeling of the Glissando Spanish corpus—0
On the Use of Different Feature Extraction Methods for Linear and Non Linear kernels—0
On the Use of Speech Recognition Techniques to Identify Bird Species—0
On the verbalization patterns of part-whole relations in isiZulu—0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks—0
On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition—0
On using 2D sequence-to-sequence models for speech recognition—0
On Using Backpropagation for Speech Texture Generation and Voice Conversion—0
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches—0
OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking—0
Open Access NAO (OAN): a ROS2-based software framework for HRI applications with the NAO robot—0
Open ASR for Icelandic: Resources and a Baseline System—0
Open Challenge for Correcting Errors of Speech Recognition Systems—0
OpenDial: A Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules—0
Open-Domain Name Error Detection using a Multi-Task RNN—0
Open Ended Intelligence: The individuation of Intelligent Agents—0
Open Implementation and Study of BEST-RQ for Speech Processing—0
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models—0
Open Set Modulation Recognition Based on Dual-Channel LSTM Model—0
Open-Source Conversational AI with SpeechBrain 1.0—0
Open-Source High Quality Speech Datasets for Basque, Catalan and Galician—0
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset—0
Open Terminology Management and Sharing Toolkit for Federation of Terminology Databases—0
Open-vocabulary Keyword-spotting with Adaptive Instance Normalization—0
Operational Assessment of Keyword Search on Oral History—0
Opportunities & Challenges In Automatic Speech Recognition—0
Optimal Data Set Selection: An Application to Grapheme-to-Phoneme Conversion—0
Optimal Transport-based Adaptation in Dysarthric Speech Tasks—0
Optimising AI Training Deployments using Graph Compilers and Containers—0
Optimising Incremental Dialogue Decisions Using Information Density for Interactive Systems—0
Optimization Methods in Deep Learning: A Comprehensive Overview—0
Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification—0
Optimized Tokenization for Transcribed Error Correction—0
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation—0
Optimizing Byte-level Representation for End-to-end ASR—0
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval—0
Optimizing expected word error rate via sampling for speech recognition—0
Optimizing Generative Dialog State Tracker via Cascading Gradient Descent—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