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 651700 of 6433 papers

TitleStatusHype
An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition0
A Decidability-Based Loss Function0
An empirical assessment of deep learning approaches to task-oriented dialog management0
An Empirical Analysis of Deep Audio-Visual Models for Speech Recognition0
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons0
A brief history of AI: how to prevent another winter (a critical review)0
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis0
A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms0
An efficient text augmentation approach for contextualized Mandarin speech recognition0
Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings0
An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems0
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks0
Adding Connectionist Temporal Summarization into Conformer to Improve Its Decoder Efficiency For Speech Recognition0
A Bilingual Interactive Human Avatar Dialogue System0
An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation0
AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation0
Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR0
An Effective Training Framework for Light-Weight Automatic Speech Recognition Models0
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts0
A comparison of streaming models and data augmentation methods for robust speech recognition0
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems0
An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement0
An Effective End-to-End Modeling Approach for Mispronunciation Detection0
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain0
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features0
An Effective Context-Balanced Adaptation Approach for Long-Tailed Speech Recognition0
A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution0
Anchored Speech Recognition with Neural Transducers0
AdaScale SGD: A Scale-Invariant Algorithm for Distributed Training0
AdaPTwin: Low-Cost Adaptive Compression of Product Twins in Transformers0
An automated medical scribe for documenting clinical encounters0
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese0
A Berkeley View of Systems Challenges for AI0
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment0
A Temporal Simulator for Developing Turn-Taking Methods for Spoken Dialogue Systems0
A Token-Wise Beam Search Algorithm for RNN-T0
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering0
An Attentional Model for Speech Translation Without Transcription0
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition0
Anatomy of Industrial Scale Multilingual ASR0
An ASR-free Fluency Scoring Approach with Self-Supervised Learning0
Adaptive Parser-Centric Text Normalization0
An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to First Graders0
An Approach to Improve Robustness of NLP Systems against ASR Errors0
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition0
An Application for Building a Polish Telephone Speech Corpus0
An analysis of incorporating an external language model into a sequence-to-sequence model0
Adaptive multilingual speech recognition with pretrained models0
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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