Although the application of deep learning to automatic speech recognition (ASR) has
resulted in dramatic reductions in word error rate for languages with abundant training data …

This paper proposes a multilingual acoustic modeling approach for Indian languages using
a Multitask Learning (MTL) framework. Language-specific phoneme recognition is explored …

The large amount of information stored in audio and video repositories makes search on
speech (SoS) a challenging area that is continuously receiving much interest. Within SoS …

The application of deep neural networks to the task of acoustic modeling for automatic
speech recognition has resulted in dramatic decreases in ASR word error rates, enabling …

Automatic Speech Recognition (ASR) technology has witnessed significant advancements
in recent years, revolutionizing human-computer interactions. While major languages have …

Despite the growing advancements in Automatic Speech Recognition (ASR) models, the
development of robust models for underrepresented languages, such as Nepali, remains a …

The application of deep neural networks to the task of acoustic modeling for automatic
speech recognition (ASR) has resulted in dramatic decreases of word error rates, allowing …

Automatic Speech Recognition (ASR) is a cross-disciplinary field that enablescomputerstoprocesshumanspeechi…
meaning of it with the help of other NLP technologies such as natural language …

Electrolarynx (EL) is a communicative aid for the patient after laryngectomy to generate
communicable speech. Since EL speech exhibits low speech intelligibility and produces …

Nowadays, a broad range of speech recognition technologies (such as Apple Siri and
Amazon Alexa) are developed as the user interface has become ever convenient and …