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Found 9 papers, 1 papers with code
Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognition
For a low latency scenario with an average latency of 80 ms, Emformer achieves WER $3. 01\%$ on test-clean and $7. 09\%$ on test-other.
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models
Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming Transformer Transducer Based Speech Recognition Using Non-Causal Convolution
This paper improves the streaming transformer transducer for speech recognition by using non-causal convolution.
Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study
Detection of common events and scenes from audio is useful for extracting and understanding human contexts in daily life.
Multi-Head State Space Model for Speech Recognition
State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches.
Ranked #8 on
Speech Recognition
on LibriSpeech test-clean
Prompting Large Language Models with Speech Recognition Abilities
Furthermore, we perform ablation studies to investigate whether the LLM can be completely frozen during training to maintain its original capabilities, scaling up the audio encoder, and increasing the audio encoder striding to generate fewer embeddings.
Abstractive Text Summarization
Automatic Speech Recognition
+3
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models
Results demonstrate that our TODM Supernet either matches or surpasses the performance of manually tuned models by up to a relative of 3% better in word error rate (WER), while efficiently keeping the cost of training many models at a small constant.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs
In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of original LLM capabilities, without using any carefully curated paired data.
Effective internal language model training and fusion for factorized transducer model
However, even with the adoption of factorized transducer models, limited improvement has been observed compared to shallow fusion.
