MingLunHan/CIF-ColDec
[ICASSP 2022] Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection
This project helps improve the accuracy of speech recognition systems, especially when dealing with specific names, technical terms, or domain-specific vocabulary. It takes audio input and a list of words or phrases that are likely to appear in the speech, then produces a more accurate transcription by prioritizing those contextual clues. This is useful for anyone working with automated speech-to-text conversion where precise recognition of certain terms is critical.
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Use this if your automated speech recognition (ASR) system frequently misinterprets proper nouns, unique product names, or jargon relevant to your industry.
Not ideal if you are looking for a general-purpose ASR solution without the need for specific contextual biasing, or if you don't have defined lists of terms to guide the recognition.
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Apache-2.0
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Last pushed
May 18, 2023
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