Accents pose significant challenges for speech recognition systems. Although joint
automatic speech recognition (ASR) and accent recognition (AR) training has been proven …

A major challenge involved in automatic dysarthria severity classification for patients with
Amyotrophic Lateral Sclerosis (ALS) is the difficulty to build a speech corpus which is large …

Bilingual English speakers speak English as one of their languages. Their English is of a
non-native kind, and their conversations are of a code-mixed fashion. The intelligibility of a …

In this paper, we present a 170.83 hour Indian English spontaneous speech dataset. Lack of
Indian English speech data is one of the major hindrances in developing robust speech …

Articulatory speech synthesis is a challenging task which requires mapping of time-varying
articulatory trajectories and speech. In recent years, deep learning methods have been …

Speech systems are sensitive to accent variations. This is especially challenging in the
Indian context, with an abundance of languages but a dearth of linguistic studies …

With the availability of large data, ASRs perform well on native English but poorly for non-
native English data. Training nonnative ASRs or adapting a native English ASR is often …

Indian English (IE), on the surface, seems quite similar to standard English. However, closer
observation shows that it has actually been influenced by the surrounding vernacular …

This paper is an attempt to address to the problem of native language in a mixed voice
environment. G-Cocktail would aid these applications in identifying commands given in …

Abstract Analysis of Indian English (IE) pronunciation variabilities is useful in ASR and TTS
modelling for the Indian context. Prior works characterised IE variabilities by reporting …