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🤯 Did You Know (click to read)
Speech-Transformer models outperform traditional RNN-based models on datasets like LibriSpeech.
Speech Transformers apply self-attention to audio frames or spectrograms, modeling temporal dependencies. Encoder-decoder frameworks convert input audio into textual output, capturing long-range correlations efficiently. Parallelization reduces training time compared to RNN-based speech models.
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💥 Impact (click to read)
Speech recognition systems achieve higher accuracy and faster training times using Transformers, supporting applications in virtual assistants and transcription services.
Developers and researchers can implement scalable, real-time speech recognition with improved robustness to noise and varying speech patterns.
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