We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

One of the major remaining challenges in modern automatic speech recognition (ASR)
systems for English is to be able to handle speech from users with a diverse set of accents …

Local dialects influence people to pronounce words of the same language differently from
each other. The great variability and complex characteristics of accents creates a major …

The performance of automatic speech recognition systems degrades with increasing
mismatch between the training and testing scenarios. Differences in speaker accents are a …

Automatic identification of foreign accents is valuable for many speech systems, such as
speech recognition, speaker identification, voice conversion, etc. The INTERSPEECH 2016 …

In this paper, we present an in-depth study on the classification of regional accents in
Mandarin speech. Experiments are carried out on Mandarin speech data systematically …

A major challenge in Automatic Speech Recognition (ASR) systems is to handle speech
from a diverse set of accents. A model trained using a single accent performs rather poorly …

In this paper, we propose an end-to-end (E2E) dialect identification system trained using
transfer learning from a multilingual automatic speech recognition (ASR) model. This is also …

As a multi-ethnic country with a large population, China is endowed with diverse dialects,
which brings considerable challenges to speech recognition work. In fact, due to …

In this work, we present a detailed analysis of how accent information is reflected in the
internal representation of speech in an end-to-end automatic speech recognition (ASR) …