Oodles AI delivers scalable, secure, and high-accuracy Automatic Speech Recognition systems using modern deep learning models, real-time streaming pipelines, and multilingual speech engines.
Automatic Speech Recognition (ASR), also referred to as Speech-to-Text (STT), is an AI-driven technology that converts spoken audio into accurate, structured text using neural networks and acoustic language models.
At Oodles AI, our ASR systems are built using transformer-based deep learning architectures, large-scale multilingual datasets, and GPU-accelerated inference pipelines to handle accents, noisy audio, and domain-specific vocabulary.
Low-latency speech-to-text processing using WebSockets and streaming ASR engines.
Support for 100+ languages using pre-trained and fine-tuned acoustic models.
Neural speaker segmentation to identify and label speakers in conversations.
Domain-specific fine-tuning using healthcare, legal, and enterprise datasets.
On-premise and private cloud ASR deployment for sensitive audio data.
Automatic punctuation, timestamps, and formatting for clean transcripts.
Real-time transcription, sentiment analysis, compliance monitoring, and agent assist.
Clinical speech recognition with HIPAA-compliant, medical-vocabulary-trained models.
Ultra-low latency captions for TV, webinars, and virtual events.
Natural conversation understanding for smart devices and telephony systems.
High-accuracy multi-speaker transcription with timestamps and speaker labels.
Automatic lecture transcription, searchable notes, and accessibility subtitles.
Oodles AI engineers build ASR systems using industry-proven frameworks, cloud platforms, and deep learning toolkits optimized for speech recognition workloads.
OpenAI Whisper, NVIDIA NeMo ASR, Mozilla DeepSpeech, Transformer-based acoustic models
Python, C++, JavaScript for ASR inference, APIs, and streaming pipelines
PyTorch, TensorFlow, Hugging Face Transformers, Kaldi
Docker, Kubernetes, GPU acceleration, AWS, Azure, On-Premise servers