#automatic-speech-recognition News & Analysis

5 articles tagged with #automatic-speech-recognition. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AINeutralarXiv – CS AI · 2d ago5/10

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Transcribing Children's Speech: ASR Performance and Obtaining Reliable Orthographic Transcriptions

Researchers evaluated nine automatic speech recognition (ASR) models on Dutch child speech datasets, finding that fine-tuned Whisper-medium achieved 5.54% word error rate on clean data but 70.37% on noisy data. Using an utterance-level selection method, they identified 42% of clean recordings as reliable without manual verification, achieving 98.3% precision and significantly reducing annotation overhead for child speech research.

AIBullisharXiv – CS AI · 3d ago6/10

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Data-Efficient On-Policy Distillation for Automatic Speech Recognition

Researchers demonstrate that a 0.6B-parameter ASR model trained on 100k hours of speech can achieve competitive performance with larger models through teacher-guided on-policy distillation, reducing the audio data requirements by 99.5% compared to industry standards while closing the capability gap with 1.7B parameter models.

AIBullishMarkTechPost · Mar 266/10

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Cohere AI Releases Cohere Transcribe: A SOTA Automatic Speech Recognition (ASR) Model Powering Enterprise Speech Intelligence

Cohere AI has released Cohere Transcribe, a new state-of-the-art Automatic Speech Recognition (ASR) model designed for enterprise applications. This marks the company's expansion beyond text generation and embedding models into the speech recognition market, targeting enterprise speech intelligence solutions.

🏢 Cohere

AIBullisharXiv – CS AI · Mar 26/1015

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Audio-Conditioned Diffusion LLMs for ASR and Deliberation Processing

Researchers developed Whisper-LLaDA, a diffusion-based large language model for automatic speech recognition that achieves 12.3% relative improvement over baseline models. The study demonstrates that audio-conditioned embeddings are crucial for accuracy improvements, while plain-text processing without acoustic features fails to enhance performance.

AIBullisharXiv – CS AI · Feb 275/103

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Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignment

Researchers developed Lipi-Ghor-882, an 882-hour Bengali speech dataset, and demonstrated that targeted fine-tuning with synthetic acoustic degradation significantly improves automatic speech recognition for long-form Bengali audio. Their dual pipeline achieved a 0.019 Real-Time Factor, establishing new benchmarks for low-resource speech processing.