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Found 5 papers, 0 papers with code
Investigation of Training Label Error Impact on RNN-T
In this paper, we propose an approach to quantitatively analyze impacts of different training label errors to RNN-T based ASR models.
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition
Acoustic models in real-time speech recognition systems typically stack multiple unidirectional LSTM layers to process the acoustic frames over time.
Data Techniques For Online End-to-end Speech Recognition
Practitioners often need to build ASR systems for new use cases in a short amount of time, given limited in-domain data.
End-to-end Anchored Speech Recognition
The anchored segment refers to the wake-up word part of an audio stream, which contains valuable speaker information that can be used to suppress interfering speech and background noise.
Maximum a Posteriori Adaptation of Network Parameters in Deep Models
We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance.
