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 61016150 of 6433 papers

TitleStatusHype
BUT System for the MLC-SLM Challenge0
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts0
Byte-based Neural Machine Translation0
Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition0
Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes0
Cache-Augmented Latent Topic Language Models for Speech Retrieval0
CacheNet: A Model Caching Framework for Deep Learning Inference on the Edge0
CAFE A Novel Code switching Dataset for Algerian Dialect French and English0
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection0
Calibration of Phone Likelihoods in Automatic Speech Recognition0
Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down0
Can Discourse Relations be Identified Incrementally?0
Can Generative Large Language Models Perform ASR Error Correction?0
Can neural networks predict dynamics they have never seen?0
Can Pretrained Neural Networks Detect Anatomy?0
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition0
Can spontaneous spoken language disfluencies help describe syntactic dependencies? An empirical study0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin0
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling0
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition0
Can Whisper perform speech-based in-context learning?0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers0
Can you hear me now? Sensitive comparisons of human and machine perception0
Can You Repeat That? Using Word Repetition to Improve Spoken Term Detection0
Capitalization and Punctuation Restoration: a Survey0
Capturing Multi-Resolution Context by Dilated Self-Attention0
Careful Whisper -- leveraging advances in automatic speech recognition for robust and interpretable aphasia subtype classification0
CarneliNet: Neural Mixture Model for Automatic Speech Recognition0
CASA-ASR: Context-Aware Speaker-Attributed ASR0
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition0
Cascaded Cross-Modal Transformer for Request and Complaint Detection0
Cascaded encoders for unifying streaming and non-streaming ASR0
Cascaded Models With Cyclic Feedback For Direct Speech Translation0
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter0
CASSANDRA: A multipurpose configurable voice-enabled human-computer-interface0
CASS-NAT: CTC Alignment-based Single Step Non-autoregressive Transformer for Speech Recognition0
CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency0
Causal Analysis of ASR Errors for Children: Quantifying the Impact of Physiological, Cognitive, and Extrinsic Factors0
Causal Structure Discovery for Error Diagnostics of Children's ASR0
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting0
CEASR: A Corpus for Evaluating Automatic Speech Recognition0
Chain-based Discriminative Autoencoders for Speech Recognition0
Chain of Correction for Full-text Speech Recognition with Large Language Models0
Chain-of-Thought Prompting for Speech Translation0
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems0
Challenges and Insights: Exploring 3D Spatial Features and Complex Networks on the MISP Dataset0
Challenges and Obstacles Towards Deploying Deep Learning Models on Mobile Devices0
Challenges and Opportunities in Multi-device Speech Processing0
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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