Apple's Machine Learning Research team has developed a new approach to automatic speech recognition (ASR) error correction using compact seq2seq models. These models, trained on real and synthetic ASR errors, significantly outperform large language models (LLMs) in terms of efficiency and accuracy, particularly in low-error scenarios. Meanwhile, Cohere has introduced Transcribe, a 2-billion parameter ASR model that reportedly surpasses Whisper Large V3 in speed and precision, especially with Arabic dialects. AI
IMPACT Specialized ASR models offer improved efficiency and accuracy, potentially reducing latency and hallucination issues in speech recognition applications.
RANK_REASON The cluster contains a research paper from Apple's ML Research team and a mention of a new ASR model from Cohere.
Read on Apple Machine Learning Research →
- Erik McDermott
- Large Language Models
- LibriSpeech
- Navdeep Jaitly
- Richard He Bai
- Ronan Collobert
- Tatiana Likhomanenko
- Cohere
- Whisper Large V3
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