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 201–250 of 6433 papers

TitleStatusHype
Whispering in Amharic: Fine-tuning Whisper for Low-resource Language—0
From S4 to Mamba: A Comprehensive Survey on Structured State Space Models—0
Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communication—0
SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
Evaluating ASR Confidence Scores for Automated Error Detection in User-Assisted Correction Interfaces—0
Halving transcription time: A fast, user-friendly and GDPR-compliant workflow to create AI-assisted transcripts for content analysis—0
MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech TokensCode1
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA—0
Whisper Speaker Identification: Leveraging Pre-Trained Multilingual Transformers for Robust Speaker EmbeddingsCode1
Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models—0
ValSub: Subsampling Validation Data to Mitigate Forgetting during ASR Personalization—0
Quantization for OpenAI's Whisper Models: A Comparative AnalysisCode0
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
Lend a Hand: Semi Training-Free Cued Speech Recognition via MLLM-Driven Hand Modeling for Barrier-free CommunicationCode0
An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR—0
Automatic Speech Recognition for Non-Native English: Accuracy and Disfluency Handling—0
Building English ASR model with regional language support—0
Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs—0
A Noise-Robust Turn-Taking System for Real-World Dialogue Robots: A Field ExperimentCode2
Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Speech RepresentationsCode1
A Causal Inference Approach for Quantifying Research Impact—0
Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning—0
From Voice to Safety: Language AI Powered Pilot-ATC Communication Understanding for Airport Surface Movement Collision Risk Assessment—0
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations—0
CORDIC Is All You Need—0
Direct Speech to Speech Translation: A Review—0
Fine-Tuning Whisper for Inclusive Prosodic Stress Analysis—0
Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems—0
UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation—0
LiteASR: Efficient Automatic Speech Recognition with Low-Rank ApproximationCode2
CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASRCode2
Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications—0
Nexus: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision—0
CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition—0
Exploring Gender Disparities in Automatic Speech Recognition Technology—0
Balancing Speech Understanding and Generation Using Continual Pre-training for Codec-based Speech LLM—0
Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation—0
Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN CorpusCode0
Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration—0
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages—0
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders—0
Retrieval-Augmented Speech Recognition Approach for Domain Challenges—0
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models—0
Moshi Moshi? A Model Selection Hijacking Adversarial Attack—0
Measuring the Effect of Transcription Noise on Downstream Language Understanding TasksCode0
Adopting Whisper for Confidence Estimation—0
Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models—0
On the Robust Approximation of ASR Metrics—0
Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization—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