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 5251–5300 of 6433 papers

TitleStatusHype
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks—0
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings—0
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition—0
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation—0
A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings—0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition—0
A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced data—0
A comparison of end-to-end models for long-form speech recognition—0
A Comparison of Hybrid and End-to-End Models for Syllable Recognition—0
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition—0
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese—0
A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit—0
A comparison of streaming models and data augmentation methods for robust speech recognition—0
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons—0
A Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition—0
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition—0
A comprehensive analysis on attention models—0
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU—0
A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR—0
A comprehensive study of batch construction strategies for recurrent neural networks in MXNet—0
A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition—0
A comprehensive study of speech separation: spectrogram vs waveform separation—0
A Comprehensive Study of the Current State-of-the-Art in Nepali Automatic Speech Recognition Systems—0
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
A Comprehensive Survey on Bengali Phoneme Recognition—0
A Computer Assisted Speech Transcription System—0
A Conditional Random Field-based Traditional Chinese Base Phrase Parser for SIGHAN Bake-off 2012 Evaluation—0
A Configurable Multilingual Model is All You Need to Recognize All Languages—0
A Conformer Based Acoustic Model for Robust Automatic Speech Recognition—0
A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation—0
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy—0
A Conventional Orthography for Tunisian Arabic—0
A Convexity-based Generalization of Viterbi for Non-Deterministic Weighted Automata—0
A Convolutional Neural Network Based Approach to Recognize Bangla Spoken Digits from Speech Signal—0
A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition—0
A Corpus and Phonetic Dictionary for Tunisian Arabic Speech Recognition—0
A Corpus for a Gesture-Controlled Mobile Spoken Dialogue System—0
A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts—0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation—0
A Corpus of Spontaneous Speech in Lectures: The KIT Lecture Corpus for Spoken Language Processing and Translation—0
A cost minimization approach to fix the vocabulary size in a tokenizer for an End-to-End ASR system—0
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition—0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech—0
Acoustic-aware Non-autoregressive Spell Correction with Mask Sample Decoding—0
Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework—0
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition—0
Acoustic feature learning using cross-domain articulatory measurements—0
Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment—0
Acoustic Model Compression with MAP adaptation—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