https://github.com/briansidp/whisperbiasing
공식 코드Can Contextual Biasing Remain Effective with Whisper and GPT-2?
Papers with Code 아카이브
Automatic Speech Recognition (ASR)
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Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/burchim/avec
공식 코드Audio-Visual Efficient Conformer for Robust Speech Recognition
https://github.com/burchim/efficientconformer
공식 코드Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognition
https://github.com/c3imaging/child_asr_conformer
공식 코드https://github.com/c3imaging/child_tts_fastpitch
공식 코드Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer Learning
https://github.com/c3imaging/whisper_child_speech
공식 코드https://github.com/cactuswiththoughts/unsupasrtheory
공식 코드https://github.com/changlee0903/d4am
공식 코드D4AM: A General Denoising Framework for Downstream Acoustic Models
https://github.com/chaufanglin/Normal2Whisper
공식 코드Improving Whispered Speech Recognition Performance using Pseudo-whispered based Data Augmentation
https://github.com/chenzhuo1011/libri_css
공식 코드https://github.com/chinjouli/dysaug-vc
공식 코드https://github.com/choko/learning_to_adapt
공식 코드Learning to adapt: a meta-learning approach for speaker adaptation
https://github.com/cic4k/wisebe
공식 코드https://github.com/circleredrain/avger
공식 코드Listening and Seeing Again: Generative Error Correction for Audio-Visual Speech Recognition
https://github.com/claritychallenge/clarity/tree/main/recipes/cpc1/e029_sheffield
공식 코드https://github.com/clovaai/textual-kd-slu
공식 코드Two-stage Textual Knowledge Distillation for End-to-End Spoken Language Understanding
https://github.com/cmu-llab/s3m-aave
공식 코드Self-supervised Speech Representations Still Struggle with African American Vernacular English
https://github.com/cs20s030/stable_distillation
공식 코드https://github.com/csalt-research/accented-codebooks-asr
공식 코드https://github.com/csikasote/BembaASR
공식 코드BembaSpeech: A Speech Recognition Corpus for the Bemba Language
https://github.com/csikasote/BembaSpeech
공식 코드BembaSpeech: A Speech Recognition Corpus for the Bemba Language
https://github.com/ctaguchi/asrcomplexity
공식 코드https://github.com/ctaguchi/killkan
공식 코드Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information
https://github.com/cuhealthybrains/mt-llm
공식 코드Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions
https://github.com/cyfer0618/Vaksanca
공식 코드Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights
https://github.com/cylumn/embodied-multimodal-asr
공식 코드Multimodal Speech Recognition for Language-Guided Embodied Agents
https://github.com/cywang97/StreamingTransformer
공식 코드On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition
https://github.com/danielajisafe/ASR-Nigeria-Pidgin
공식 코드Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin
https://github.com/daniellin94144/dual-textless-sqa
공식 코드DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
https://github.com/daniellin94144/test-time-adaptation-asr-suta
공식 코드https://github.com/danielyuenhx/asdf-differential-testing
공식 코드ASDF: A Differential Testing Framework for Automatic Speech Recognition Systems
https://github.com/dataoceanai/dolphin
공식 코드Dolphin: A Large-Scale Automatic Speech Recognition Model for Eastern Languages
https://github.com/dcgm/softctc
공식 코드SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels
https://github.com/diabolocom-research/conversationaldataset
공식 코드ASR Benchmarking: Need for a More Representative Conversational Dataset
https://github.com/diamondfan/fairseq
공식 코드Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR
https://github.com/didzis/tensorflowAMR
공식 코드Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.