Speech recognition is the task of recognising speech within audio and converting it into text.
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In automatic speech recognition, often little training data is available for specific challenging tasks, but training of state-of-the-art automatic speech recognition systems requires large amounts of annotated speech.
This paper presents our latest investigation on end-to-end automatic speech recognition (ASR) for overlapped speech.
It is the subject of the main result of this article to provide space-time error estimates for DNN approximations of Euler approximations of certain perturbed differential equations.
By using techniques like smoothing and interpolation of pre-processed data with supervised and unsupervised stemming, different issues in language model for Indian language: Telugu has been addressed.
Out-of-domain ASR systems can be applied to perform speaker adaptation with untranscribed training data of the target language, and to decode the training speech into frame-level labels for DNN training.
This paper proposes that the community place focus on the MALACH corpus to develop speech recognition systems that are more robust with respect to accents, disfluencies and emotional speech.
The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional state, confidence and stress levels, physical condition, age, gender, and personal traits.